The Cloud Show with Magnus Mårtensson ft. Anurag Karuparti & Paul S - Ep: 37

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hello everyone and welcome back to
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hello everyone and welcome back to
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hello everyone and welcome back to another episode of the cloud show today
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another episode of the cloud show today
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another episode of the cloud show today I'm going to talk to Two Gentlemen who
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I'm going to talk to Two Gentlemen who
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I'm going to talk to Two Gentlemen who are actually I think it's maybe the
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are actually I think it's maybe the
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are actually I think it's maybe the first time I have two people on my show
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first time I have two people on my show
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first time I have two people on my show it's cool so we're GNA talk about
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it's cool so we're GNA talk about
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it's cool so we're GNA talk about something that I believe this industry
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something that I believe this industry
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something that I believe this industry right now can't get enough of talking
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right now can't get enough of talking
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right now can't get enough of talking about we are going to talk about
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about we are going to talk about
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about we are going to talk about generative Ai and two experts are with
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generative Ai and two experts are with
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generative Ai and two experts are with me on the cloud show today who have
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me on the cloud show today who have
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me on the cloud show today who have written a book on the topic and who
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written a book on the topic and who
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written a book on the topic and who works with this uh extensively and and
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works with this uh extensively and and
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works with this uh extensively and and and in detail so the experts the stars
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and in detail so the experts the stars
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and in detail so the experts the stars of the show tonight is Paul and
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of the show tonight is Paul and
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of the show tonight is Paul and [Music]
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Anu hello gentlemen hello how are you
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Anu hello gentlemen hello how are you
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Anu hello gentlemen hello how are you hello hi Magnus hi everyone it's good
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hello hi Magnus hi everyone it's good
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hello hi Magnus hi everyone it's good it's good to have you on the show
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it's good to have you on the show
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it's good to have you on the show Welcome to the cloud show excited to be
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Welcome to the cloud show excited to be
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Welcome to the cloud show excited to be here thank you for having us brilant
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here thank you for having us brilant
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here thank you for having us brilant thank you yeah let's let's let's into
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thank you yeah let's let's let's into
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thank you yeah let's let's let's into into it uh straight away and and um int
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into it uh straight away and and um int
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into it uh straight away and and um int through you guys who are you and what
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through you guys who are you and what
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through you guys who are you and what what do you
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what do you
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what do you do sure thank you Magnus uh I I could
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do sure thank you Magnus uh I I could
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do sure thank you Magnus uh I I could start my name is Paul sing I'm a
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start my name is Paul sing I'm a
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start my name is Paul sing I'm a currently a principal Cloud solution
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currently a principal Cloud solution
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currently a principal Cloud solution architect with Microsoft covering data
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architect with Microsoft covering data
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architect with Microsoft covering data and AI this is a technical role so I
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and AI this is a technical role so I
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and AI this is a technical role so I provide guidance to our customers and
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provide guidance to our customers and
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provide guidance to our customers and organizations who are adopting the
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organizations who are adopting the
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organizations who are adopting the Microsoft Azure Cloud technology spe
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Microsoft Azure Cloud technology spe
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Microsoft Azure Cloud technology spe specifically around the data and AI
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specifically around the data and AI
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specifically around the data and AI domain um I focus on Professional
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domain um I focus on Professional
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domain um I focus on Professional Services companies primarily at this
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Services companies primarily at this
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Services companies primarily at this time so large Fortune 50 companies yeah
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time so large Fortune 50 companies yeah
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time so large Fortune 50 companies yeah yep y That's yeah I'm I'm I'm based in
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yep y That's yeah I'm I'm I'm based in
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yep y That's yeah I'm I'm I'm based in Northern California so you know lots of
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Northern California so you know lots of
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Northern California so you know lots of sunshine here sometimes too much and
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sunshine here sometimes too much and
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sunshine here sometimes too much and nice I'm currently working on also a
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nice I'm currently working on also a
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nice I'm currently working on also a side project for my personal time I'm
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side project for my personal time I'm
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side project for my personal time I'm volunteering for a nonprofit
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volunteering for a nonprofit
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volunteering for a nonprofit organization that's focused on stem uh
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organization that's focused on stem uh
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organization that's focused on stem uh growth they're called human bulb and
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growth they're called human bulb and
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growth they're called human bulb and what they do is they help
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what they do is they help
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what they do is they help underprivileged students and also
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underprivileged students and also
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underprivileged students and also business leaders on better understanding
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business leaders on better understanding
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business leaders on better understanding Technologies and how to adopt you know
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Technologies and how to adopt you know
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Technologies and how to adopt you know new technologies like gen for example so
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new technologies like gen for example so
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new technologies like gen for example so fantastic that's lovely okay cool
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fantastic that's lovely okay cool
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fantastic that's lovely okay cool brilliant and Anu what's up with you yes
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brilliant and Anu what's up with you yes
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brilliant and Anu what's up with you yes similar to Paul I'm I'm a senior Cloud
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similar to Paul I'm I'm a senior Cloud
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similar to Paul I'm I'm a senior Cloud solution architect specializing in data
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solution architect specializing in data
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solution architect specializing in data and AI based out of New York and it's
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and AI based out of New York and it's
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and AI based out of New York and it's quite hot here
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quite hot here
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quite hot here today and uh yeah so as a cloud
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today and uh yeah so as a cloud
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today and uh yeah so as a cloud architect uh so I'm helping the the SE
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architect uh so I'm helping the the SE
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architect uh so I'm helping the the SE Suite Executives on the data and
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Suite Executives on the data and
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Suite Executives on the data and strategy one day the next day it would
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strategy one day the next day it would
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strategy one day the next day it would be going more lower level with the
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be going more lower level with the
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be going more lower level with the engineering teams right uh helping them
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engineering teams right uh helping them
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engineering teams right uh helping them unblock on technical stuff and then also
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unblock on technical stuff and then also
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unblock on technical stuff and then also upskilling them on Azure AI products and
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upskilling them on Azure AI products and
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upskilling them on Azure AI products and services my goal is to ensure that they
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services my goal is to ensure that they
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services my goal is to ensure that they are successful on Azure products and
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are successful on Azure products and
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are successful on Azure products and services data and products and services
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services data and products and services
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services data and products and services so that's uh about my role apart from
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so that's uh about my role apart from
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so that's uh about my role apart from work I like running marathons uh and
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work I like running marathons uh and
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work I like running marathons uh and also I Mentor students in Africa like
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also I Mentor students in Africa like
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also I Mentor students in Africa like through the global mentorship initiative
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through the global mentorship initiative
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through the global mentorship initiative uh like college students so so that's a
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uh like college students so so that's a
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uh like college students so so that's a little about me that's lovely lovely
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little about me that's lovely lovely
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little about me that's lovely lovely absolutely lovely okay brilliant well
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absolutely lovely okay brilliant well
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absolutely lovely okay brilliant well it's it's it's wonderful to have you
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it's it's it's wonderful to have you
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it's it's it's wonderful to have you guys on this show thank you for being
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guys on this show thank you for being
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guys on this show thank you for being part of the cloud show and and of course
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part of the cloud show and and of course
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part of the cloud show and and of course we're going to talk uh about your book
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we're going to talk uh about your book
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we're going to talk uh about your book but I'd like to talk about what went
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but I'd like to talk about what went
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but I'd like to talk about what went into your book uh we're want to talk
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into your book uh we're want to talk
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into your book uh we're want to talk about um you know generative AI
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about um you know generative AI
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about um you know generative AI Solutions architecture that sort of
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Solutions architecture that sort of
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Solutions architecture that sort of stuff um you know what are the like the
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stuff um you know what are the like the
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stuff um you know what are the like the if you will they like the key components
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if you will they like the key components
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if you will they like the key components of this hey we have a sign let's let's
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of this hey we have a sign let's let's
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of this hey we have a sign let's let's show the book Let's Do It Let's do it
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show the book Let's Do It Let's do it
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show the book Let's Do It Let's do it straight away this is the book you see
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straight away this is the book you see
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straight away this is the book you see the names all right I see the names and
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the names all right I see the names and
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the names all right I see the names and oh he made it larger good all right so
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oh he made it larger good all right so
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oh he made it larger good all right so there there's the book generative AI for
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there there's the book generative AI for
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there there's the book generative AI for cloud Solutions so let's talk about that
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cloud Solutions so let's talk about that
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cloud Solutions so let's talk about that like what is this uh when you're
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like what is this uh when you're
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like what is this uh when you're building generative AI Solutions and
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building generative AI Solutions and
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building generative AI Solutions and you're architecting this what goes into
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you're architecting this what goes into
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you're architecting this what goes into something like
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something like
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something like that sure any do you want to
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that sure any do you want to
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that sure any do you want to start sure uh so yeah so this is this
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start sure uh so yeah so this is this
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start sure uh so yeah so this is this book is basically it incorporates our
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book is basically it incorporates our
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book is basically it incorporates our experiences from the field but when we
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experiences from the field but when we
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experiences from the field but when we were directly working with the customers
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were directly working with the customers
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were directly working with the customers right so last one and a half years have
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right so last one and a half years have
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right so last one and a half years have been like extremely rewarding for us in
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been like extremely rewarding for us in
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been like extremely rewarding for us in terms of learning like moving so many
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terms of learning like moving so many
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terms of learning like moving so many generative solutions to production like
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generative solutions to production like
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generative solutions to production like I I I have like 23 customers across
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I I I have like 23 customers across
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I I I have like 23 customers across media entertainment and real estate uh
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media entertainment and real estate uh
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media entertainment and real estate uh helped around 50 plus use cases we
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helped around 50 plus use cases we
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helped around 50 plus use cases we guided them to production and so this is
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guided them to production and so this is
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guided them to production and so this is this book consists of all our
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this book consists of all our
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this book consists of all our experiences Lessons Learned and how do
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experiences Lessons Learned and how do
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experiences Lessons Learned and how do you build that robust architecture right
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you build that robust architecture right
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you build that robust architecture right gen architecture we also spoken from the
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gen architecture we also spoken from the
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gen architecture we also spoken from the core principle standpoint the first
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core principle standpoint the first
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core principle standpoint the first principles like rag fine tuning when do
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principles like rag fine tuning when do
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principles like rag fine tuning when do you use it right you don't need it all
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you use it right you don't need it all
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you use it right you don't need it all the time when do you use it one over the
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the time when do you use it one over the
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the time when do you use it one over the other different typ of model we spoke
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other different typ of model we spoke
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other different typ of model we spoke about multimodal uh yeah and how to
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about multimodal uh yeah and how to
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about multimodal uh yeah and how to operationalize it and also yeah yeah
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operationalize it and also yeah yeah
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operationalize it and also yeah yeah Paul you want to add yeah yeah sure of
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Paul you want to add yeah yeah sure of
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Paul you want to add yeah yeah sure of course uh of course with any technical
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course uh of course with any technical
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course uh of course with any technical Endeavor for an organization there's the
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Endeavor for an organization there's the
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Endeavor for an organization there's the business strategy that's involved so we
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business strategy that's involved so we
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business strategy that's involved so we want to you know talk about some of the
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want to you know talk about some of the
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want to you know talk about some of the areas where there's organizational
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areas where there's organizational
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areas where there's organizational changes that occur when adopting new
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changes that occur when adopting new
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changes that occur when adopting new technologies such as gen AI yeah and
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technologies such as gen AI yeah and
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technologies such as gen AI yeah and this is moving fast now right I mean
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this is moving fast now right I mean
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this is moving fast now right I mean it's it's generative AI it's moving
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it's it's generative AI it's moving
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it's it's generative AI it's moving speed of light faster even it's so fast
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speed of light faster even it's so fast
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speed of light faster even it's so fast and it's growing so much and and and
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and it's growing so much and and and
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and it's growing so much and and and everybody I guess wants to get in on
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everybody I guess wants to get in on
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everybody I guess wants to get in on that like if you had a product before
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that like if you had a product before
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that like if you had a product before and if it doesn't have the product but
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and if it doesn't have the product but
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and if it doesn't have the product but now with AI you're not part you're not
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now with AI you're not part you're not
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now with AI you're not part you're not on the you're not on the train right
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on the you're not on the train right
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on the you're not on the train right it's like you're at the
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it's like you're at the
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it's like you're at the station yeah 100% I mean we see every
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station yeah 100% I mean we see every
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station yeah 100% I mean we see every organization trying to at least
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organization trying to at least
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organization trying to at least understand and learn generative Ai and
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understand and learn generative Ai and
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understand and learn generative Ai and trying to adopt it into the business
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trying to adopt it into the business
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trying to adopt it into the business processes I mean there's huge rewards
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processes I mean there's huge rewards
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processes I mean there's huge rewards there's a lot of companies that have
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there's a lot of companies that have
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there's a lot of companies that have done some the return on investment as
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done some the return on investment as
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done some the return on investment as well to
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well to
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well to yeah can make their users their internal
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yeah can make their users their internal
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yeah can make their users their internal more productive yeah so I guess that's
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more productive yeah so I guess that's
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more productive yeah so I guess that's that's part of it I
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that's part of it I
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that's part of it I mean using a service that somebody else
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mean using a service that somebody else
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mean using a service that somebody else created that is um that has added AI
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created that is um that has added AI
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created that is um that has added AI capabilities like you know Office 365
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capabilities like you know Office 365
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capabilities like you know Office 365 you could use co-pilots and things right
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you could use co-pilots and things right
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you could use co-pilots and things right um that's one thing but taking something
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um that's one thing but taking something
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um that's one thing but taking something AI into your own like organization and
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AI into your own like organization and
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AI into your own like organization and building something for you based on
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building something for you based on
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building something for you based on whatever business you're doing that's
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whatever business you're doing that's
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whatever business you're doing that's something else entirely um and and and
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something else entirely um and and and
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something else entirely um and and and how do you go about as a business to
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how do you go about as a business to
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how do you go about as a business to understand uh like we should also have
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understand uh like we should also have
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understand uh like we should also have some AI I guess because everybody says
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some AI I guess because everybody says
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some AI I guess because everybody says that everybody has to have that and what
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that everybody has to have that and what
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that everybody has to have that and what should we do
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should we do
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should we do now what's the what's the cat how do you
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now what's the what's the cat how do you
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now what's the what's the cat how do you get going with
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get going with
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get going with this so so what I would I mean other
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this so so what I would I mean other
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this so so what I would I mean other than buying the book and and reading
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than buying the book and and reading
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than buying the book and and reading through because we did sign we our
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through because we did sign we our
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through because we did sign we our approach was to you know that new and
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approach was to you know that new and
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approach was to you know that new and understanding but primarily it's like if
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understanding but primarily it's like if
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understanding but primarily it's like if you don't understand the technology you
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you don't understand the technology you
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you don't understand the technology you don't have to be an expert at it however
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don't have to be an expert at it however
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don't have to be an expert at it however I would recommend getting that basic
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I would recommend getting that basic
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I would recommend getting that basic understanding YouTube videos or other
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understanding YouTube videos or other
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understanding YouTube videos or other platforms there's plenty of free
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platforms there's plenty of free
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platforms there's plenty of free documents out there videos that are
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documents out there videos that are
7:08
documents out there videos that are available um watching Cloud shows you
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available um watching Cloud shows you
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available um watching Cloud shows you know of course um definely yeah so
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know of course um definely yeah so
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know of course um definely yeah so that's understanding the the underlying
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that's understanding the the underlying
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that's understanding the the underlying principles is so so is there sort of a
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principles is so so is there sort of a
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principles is so so is there sort of a structured approach for a a a company to
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structured approach for a a a company to
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structured approach for a a a company to like okay we we want to figure out AI
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like okay we we want to figure out AI
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like okay we we want to figure out AI for us what it means for us for our
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for us what it means for us for our
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for us what it means for us for our business like how do you approach
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business like how do you approach
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business like how do you approach approach that is there like a breakdown
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approach that is there like a breakdown
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approach that is there like a breakdown thing you can do or
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thing you can do or
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thing you can do or yeah so yeah I think the first thing is
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yeah so yeah I think the first thing is
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yeah so yeah I think the first thing is to go do like a comprehensive ideation
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to go do like a comprehensive ideation
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to go do like a comprehensive ideation phase right and what we observed was
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phase right and what we observed was
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phase right and what we observed was hackathons were very successful they
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hackathons were very successful they
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hackathons were very successful they were like breeding grounds for Creative
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were like breeding grounds for Creative
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were like breeding grounds for Creative Solutions innovative solutions it got a
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Solutions innovative solutions it got a
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Solutions innovative solutions it got a lot of innovation out of the employees
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lot of innovation out of the employees
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lot of innovation out of the employees but you will see so many use cases come
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but you will see so many use cases come
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but you will see so many use cases come out of these hackathons you don't want
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out of these hackathons you don't want
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out of these hackathons you don't want to do everything you just want to narrow
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to do everything you just want to narrow
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to do everything you just want to narrow down on few of them that add value to
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down on few of them that add value to
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down on few of them that add value to your to your industry to your business
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your to your industry to your business
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your to your industry to your business right that may be productivity boost uh
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right that may be productivity boost uh
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right that may be productivity boost uh so one of the things we saw was the
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so one of the things we saw was the
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so one of the things we saw was the internal co-pilots like using Azure
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internal co-pilots like using Azure
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internal co-pilots like using Azure opener on your data that was a big hit
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opener on your data that was a big hit
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opener on your data that was a big hit and those Solutions picked up because it
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and those Solutions picked up because it
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and those Solutions picked up because it was like quick time to Val to Quick time
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was like quick time to Val to Quick time
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was like quick time to Val to Quick time to Market and like it was quick wins and
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to Market and like it was quick wins and
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to Market and like it was quick wins and it gave immediate immediate value and
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it gave immediate immediate value and
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it gave immediate immediate value and and we we saw that a lot mhm so
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and we we saw that a lot mhm so
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and we we saw that a lot mhm so essentially hack away like gather people
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essentially hack away like gather people
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essentially hack away like gather people hack away take some of your data and and
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hack away take some of your data and and
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hack away take some of your data and and try to like do something not production
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try to like do something not production
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try to like do something not production product yet but figure out what to do
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product yet but figure out what to do
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product yet but figure out what to do corre get get comfortable with the tools
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corre get get comfortable with the tools
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corre get get comfortable with the tools and the development process and if
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and the development process and if
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and the development process and if there's any gaps in the organization
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there's any gaps in the organization
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there's any gaps in the organization such as knowledge training then you
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such as knowledge training then you
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such as knowledge training then you identify those during the hackathon okay
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identify those during the hackathon okay
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identify those during the hackathon okay okay and so what when when companies are
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okay and so what when when companies are
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okay and so what when when companies are doing like you like you've mentioned
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doing like you like you've mentioned
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doing like you like you've mentioned here you have been doing this with a lot
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here you have been doing this with a lot
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here you have been doing this with a lot of companies and and a lot of projects
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of companies and and a lot of projects
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of companies and and a lot of projects um what are some typical
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um what are some typical
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um what are some typical challenges pitfalls problems that that
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challenges pitfalls problems that that
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challenges pitfalls problems that that that that your customers run into what
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that that your customers run into what
9:05
that that your customers run into what what is hard here except learning the
9:08
what is hard here except learning the
9:08
what is hard here except learning the technology I guess but that's you know
9:10
technology I guess but that's you know
9:10
technology I guess but that's you know that's learning yeah well that that's
9:13
that's learning yeah well that that's
9:13
that's learning yeah well that that's exactly it is I mean so some of is human
9:15
exactly it is I mean so some of is human
9:15
exactly it is I mean so some of is human resource component as well make sure you
9:17
resource component as well make sure you
9:17
resource component as well make sure you have the right staff some have have to
9:19
have the right staff some have have to
9:19
have the right staff some have have to have developer background because you
9:21
have developer background because you
9:21
have developer background because you are developing against your own data
9:23
are developing against your own data
9:23
are developing against your own data although you leverag AI in the cloud but
9:26
although you leverag AI in the cloud but
9:26
although you leverag AI in the cloud but there's still development against your
9:28
there's still development against your
9:28
there's still development against your own data um I would also some of the
9:31
own data um I would also some of the
9:31
own data um I would also some of the challenge is like things understanding
9:34
challenge is like things understanding
9:34
challenge is like things understanding the Model Behavior they're called large
9:36
the Model Behavior they're called large
9:36
the Model Behavior they're called large language models and each model has its
9:38
language models and each model has its
9:38
language models and each model has its own limitations on how much data it can
9:41
own limitations on how much data it can
9:41
own limitations on how much data it can process so if you're an organization and
9:43
process so if you're an organization and
9:43
process so if you're an organization and you want to throw a large number of
9:44
you want to throw a large number of
9:44
you want to throw a large number of queries questions we call it prompts
9:46
queries questions we call it prompts
9:46
queries questions we call it prompts against a model just understand that
9:49
against a model just understand that
9:49
against a model just understand that there are limitations on any Cloud
9:52
there are limitations on any Cloud
9:52
there are limitations on any Cloud resource so people are not understanding
9:55
resource so people are not understanding
9:55
resource so people are not understanding how to calibrate yet not initially no
9:58
how to calibrate yet not initially no
9:58
how to calibrate yet not initially no and this it's a iterative process so
10:00
and this it's a iterative process so
10:00
and this it's a iterative process so there's a lot of testing that goes on
10:01
there's a lot of testing that goes on
10:01
there's a lot of testing that goes on against their own data and against
10:03
against their own data and against
10:03
against their own data and against different types of models and newer
10:05
different types of models and newer
10:05
different types of models and newer models come out every day that are more
10:07
models come out every day that are more
10:07
models come out every day that are more efficient lower cost as well so there's
10:10
efficient lower cost as well so there's
10:10
efficient lower cost as well so there's you know some
10:12
you know some
10:12
you know some it all right so learn about these things
10:14
it all right so learn about these things
10:14
it all right so learn about these things and start um testing and and and you
10:17
and start um testing and and and you
10:17
and start um testing and and and you know iteratively try out new things new
10:19
know iteratively try out new things new
10:19
know iteratively try out new things new models different approaches correct yeah
10:22
models different approaches correct yeah
10:22
models different approaches correct yeah yeah and one more thing is like well so
10:24
yeah and one more thing is like well so
10:24
yeah and one more thing is like well so we saw a lot of rag based Solutions
10:26
we saw a lot of rag based Solutions
10:26
we saw a lot of rag based Solutions going into production and now what we're
10:28
going into production and now what we're
10:28
going into production and now what we're seeing is the customer expectations are
10:30
seeing is the customer expectations are
10:30
seeing is the customer expectations are rising on the quality of the outputs of
10:33
rising on the quality of the outputs of
10:33
rising on the quality of the outputs of these rag Solutions so what I would
10:35
these rag Solutions so what I would
10:36
these rag Solutions so what I would suggest is like having like an
10:37
suggest is like having like an
10:37
suggest is like having like an evaluation strategy early on that can
10:40
evaluation strategy early on that can
10:40
evaluation strategy early on that can really help you see surprises uh help
10:43
really help you see surprises uh help
10:43
really help you see surprises uh help you stop seeing surprises later on right
10:46
you stop seeing surprises later on right
10:46
you stop seeing surprises later on right so like doing uh initial assessment with
10:48
so like doing uh initial assessment with
10:48
so like doing uh initial assessment with different models and like having a
10:50
different models and like having a
10:50
different models and like having a qualitative quantitative metrics to
10:53
qualitative quantitative metrics to
10:53
qualitative quantitative metrics to assess the quality of the outputs that
10:55
assess the quality of the outputs that
10:55
assess the quality of the outputs that really helps early
10:56
really helps early
10:56
really helps early on okay okay all right that makes sense
10:59
on okay okay all right that makes sense
10:59
on okay okay all right that makes sense that makes sense it's it's something
11:01
that makes sense it's it's something
11:01
that makes sense it's it's something that you have to get into and and really
11:02
that you have to get into and and really
11:02
that you have to get into and and really start doing I guess to to really fully
11:05
start doing I guess to to really fully
11:05
start doing I guess to to really fully understand um this process but but
11:07
understand um this process but but
11:07
understand um this process but but essentially you're saying start doing
11:10
essentially you're saying start doing
11:10
essentially you're saying start doing something and and and then try to qu you
11:13
something and and and then try to qu you
11:13
something and and and then try to qu you know qualify it get better and and more
11:16
know qualify it get better and and more
11:16
know qualify it get better and and more focused on what you should be doing but
11:18
focused on what you should be doing but
11:18
focused on what you should be doing but you don't know from the beginning so at
11:19
you don't know from the beginning so at
11:19
you don't know from the beginning so at least do
11:20
least do
11:20
least do something let me add there's a lot of uh
11:23
something let me add there's a lot of uh
11:23
something let me add there's a lot of uh Cloud vendors that provide this
11:24
Cloud vendors that provide this
11:24
Cloud vendors that provide this playground environment where you can
11:26
playground environment where you can
11:26
playground environment where you can test against these models like Asher for
11:28
test against these models like Asher for
11:29
test against these models like Asher for example
11:29
example
11:29
example yeah definitely of course Azure Azure
11:32
yeah definitely of course Azure Azure
11:32
yeah definitely of course Azure Azure open AI you know there's the AI we're
11:34
open AI you know there's the AI we're
11:34
open AI you know there's the AI we're allowed to say
11:37
it okay brilliant so um since this space
11:40
it okay brilliant so um since this space
11:40
it okay brilliant so um since this space is kind of moving so fast because it
11:43
is kind of moving so fast because it
11:43
is kind of moving so fast because it really is there's new models coming out
11:45
really is there's new models coming out
11:45
really is there's new models coming out all the time it seems and and things are
11:47
all the time it seems and and things are
11:47
all the time it seems and and things are happening at at a at an not an alarming
11:49
happening at at a at an not an alarming
11:49
happening at at a at an not an alarming rate but a very very high rate um what
11:53
rate but a very very high rate um what
11:53
rate but a very very high rate um what are like the latest uh Trends what's
11:55
are like the latest uh Trends what's
11:55
are like the latest uh Trends what's going on right now in generative AI yeah
11:59
going on right now in generative AI yeah
11:59
going on right now in generative AI yeah K start and then I know an's got a few
12:00
K start and then I know an's got a few
12:00
K start and then I know an's got a few as well but uh so Anu mentioned rag
12:03
as well but uh so Anu mentioned rag
12:03
as well but uh so Anu mentioned rag which is retrieval augmented generation
12:05
which is retrieval augmented generation
12:05
which is retrieval augmented generation and this is using large langage models
12:07
and this is using large langage models
12:07
and this is using large langage models against your own data you know that's
12:09
against your own data you know that's
12:09
against your own data you know that's been around uh but to increase the speed
12:13
been around uh but to increase the speed
12:13
been around uh but to increase the speed and also approve in increase the
12:17
and also approve in increase the
12:17
and also approve in increase the response uh there's newer Technologies
12:19
response uh there's newer Technologies
12:19
response uh there's newer Technologies coming out such as integration with
12:21
coming out such as integration with
12:21
coming out such as integration with knowledge graphs so new and great such
12:25
knowledge graphs so new and great such
12:25
knowledge graphs so new and great such as just AI graphs a knowledge graph is
12:28
as just AI graphs a knowledge graph is
12:28
as just AI graphs a knowledge graph is it's what it is instead of using vectors
12:30
it's what it is instead of using vectors
12:30
it's what it is instead of using vectors which rag uses uh predominantly
12:32
which rag uses uh predominantly
12:32
which rag uses uh predominantly currently the it's an intersection
12:34
currently the it's an intersection
12:34
currently the it's an intersection between ji and Knowledge Graph so
12:37
between ji and Knowledge Graph so
12:37
between ji and Knowledge Graph so Knowledge Graph can gen and what and
12:40
Knowledge Graph can gen and what and
12:40
Knowledge Graph can gen and what and knowledge knowledge graphs yeah what
12:42
knowledge knowledge graphs yeah what
12:42
knowledge knowledge graphs yeah what these knowledge graphs do is they
12:44
these knowledge graphs do is they
12:44
these knowledge graphs do is they connect various data points and shows
12:46
connect various data points and shows
12:46
connect various data points and shows the relationship between those data
12:48
the relationship between those data
12:48
the relationship between those data within an
12:50
within an
12:50
within an organization that the outcome is more
12:52
organization that the outcome is more
12:52
organization that the outcome is more accurate results uh because if you do
12:55
accurate results uh because if you do
12:55
accurate results uh because if you do some prom sometimes you don't get the
12:56
some prom sometimes you don't get the
12:56
some prom sometimes you don't get the accurate results You're Expecting so
12:58
accurate results You're Expecting so
12:58
accurate results You're Expecting so we're seeing a lot
12:59
we're seeing a lot
12:59
we're seeing a lot a movement toward using more of a AI
13:02
a movement toward using more of a AI
13:03
a movement toward using more of a AI plus graph knowledge graph solutions oh
13:05
plus graph knowledge graph solutions oh
13:05
plus graph knowledge graph solutions oh interesting interesting so and and just
13:07
interesting interesting so and and just
13:07
interesting interesting so and and just so happens that Microsoft has one of the
13:10
so happens that Microsoft has one of the
13:10
so happens that Microsoft has one of the largest graph database uh products that
13:12
largest graph database uh products that
13:13
largest graph database uh products that exist uh or maybe the largest right
13:15
exist uh or maybe the largest right
13:15
exist uh or maybe the largest right Cosmos yeah oh yeah and as a matter of
13:18
Cosmos yeah oh yeah and as a matter of
13:18
Cosmos yeah oh yeah and as a matter of fact there's a a new service Cosmos AI
13:20
fact there's a a new service Cosmos AI
13:20
fact there's a a new service Cosmos AI graph that is based off of the knowledge
13:23
graph that is based off of the knowledge
13:23
graph that is based off of the knowledge graph plus AI that was not a leading
13:25
graph plus AI that was not a leading
13:25
graph plus AI that was not a leading question at all not not not even
13:29
thanks
13:31
thanks
13:31
thanks Magnus no but it's important right
13:33
Magnus no but it's important right
13:33
Magnus no but it's important right everything is being adapted to uh being
13:36
everything is being adapted to uh being
13:36
everything is being adapted to uh being able to handle these types of services
13:39
able to handle these types of services
13:39
able to handle these types of services and demands um so every Service uh is
13:43
and demands um so every Service uh is
13:43
and demands um so every Service uh is adapted to being able to to take care of
13:46
adapted to being able to to take care of
13:46
adapted to being able to to take care of this with with speed and accuracy and
13:48
this with with speed and accuracy and
13:48
this with with speed and accuracy and and you know good money right can't be
13:51
and you know good money right can't be
13:51
and you know good money right can't be too expensive as well and efficient
13:53
too expensive as well and efficient
13:53
too expensive as well and efficient correct yeah efficiency yes we have a
13:55
correct yeah efficiency yes we have a
13:55
correct yeah efficiency yes we have a few others on you want to mention yeah
13:58
few others on you want to mention yeah
13:58
few others on you want to mention yeah my my do one is autonomous agents we
14:00
my my do one is autonomous agents we
14:00
my my do one is autonomous agents we seeing like a lot of progress in this
14:02
seeing like a lot of progress in this
14:02
seeing like a lot of progress in this area so autonomous agents are basically
14:05
area so autonomous agents are basically
14:05
area so autonomous agents are basically these are intelligent AI systems that
14:07
these are intelligent AI systems that
14:07
these are intelligent AI systems that can act independently right with little
14:10
can act independently right with little
14:10
can act independently right with little or no human intervention they can uh do
14:13
or no human intervention they can uh do
14:13
or no human intervention they can uh do self feedback and improve automatically
14:15
self feedback and improve automatically
14:15
self feedback and improve automatically right so these I think will have massive
14:19
right so these I think will have massive
14:19
right so these I think will have massive productivity boost because of this and
14:21
productivity boost because of this and
14:21
productivity boost because of this and some of the use cases I see disrupting
14:24
some of the use cases I see disrupting
14:24
some of the use cases I see disrupting is like call centers you'll have like a
14:25
is like call centers you'll have like a
14:26
is like call centers you'll have like a team of autonomous agents that can
14:29
team of autonomous agents that can
14:29
team of autonomous agents that can augment human capabilities and like make
14:31
augment human capabilities and like make
14:31
augment human capabilities and like make them more productive so that's one I
14:34
them more productive so that's one I
14:34
them more productive so that's one I think it it could be helpful even like
14:35
think it it could be helpful even like
14:35
think it it could be helpful even like in education uh it could provide more
14:38
in education uh it could provide more
14:38
in education uh it could provide more personalized education to students so
14:40
personalized education to students so
14:41
personalized education to students so with like having a team of autonomous
14:42
with like having a team of autonomous
14:42
with like having a team of autonomous agents so I think that is going to be
14:45
agents so I think that is going to be
14:45
agents so I think that is going to be big and also like the market for
14:46
big and also like the market for
14:46
big and also like the market for autonomous agents is expected to grow to
14:48
autonomous agents is expected to grow to
14:48
autonomous agents is expected to grow to like close to $30 billion by 2028 in
14:51
like close to $30 billion by 2028 in
14:51
like close to $30 billion by 2028 in North America alone so that is one the
14:54
North America alone so that is one the
14:54
North America alone so that is one the second one is multimodal like we saw a
14:56
second one is multimodal like we saw a
14:56
second one is multimodal like we saw a lot of text based uh text based
15:00
lot of text based uh text based
15:00
lot of text based uh text based Solutions generative solution now it's
15:01
Solutions generative solution now it's
15:01
Solutions generative solution now it's becoming images audio and then soon
15:03
becoming images audio and then soon
15:03
becoming images audio and then soon there'll be video generation models also
15:05
there'll be video generation models also
15:05
there'll be video generation models also right and then small small language
15:07
right and then small small language
15:07
right and then small small language models is one more I I think that the
15:10
models is one more I I think that the
15:10
models is one more I I think that the trend we are seeing which is going to be
15:12
trend we are seeing which is going to be
15:12
trend we are seeing which is going to be big for Edge devices like smartphones
15:15
big for Edge devices like smartphones
15:15
big for Edge devices like smartphones and autonomous way oh yeah so having
15:17
and autonomous way oh yeah so having
15:17
and autonomous way oh yeah so having having a a small small large language
15:23
model yeah in the palm of your hand all
15:26
model yeah in the palm of your hand all
15:26
model yeah in the palm of your hand all right app Apple's working on that in
15:28
right app Apple's working on that in
15:28
right app Apple's working on that in their next
15:29
their next
15:29
their next IOS as well I think they are right huge
15:33
IOS as well I think they are right huge
15:33
IOS as well I think they are right huge it's funny how everyone everyone doesn't
15:35
it's funny how everyone everyone doesn't
15:35
it's funny how everyone everyone doesn't matter which company you are you you
15:37
matter which company you are you you
15:37
matter which company you are you you release something called whatever it was
15:39
release something called whatever it was
15:39
release something called whatever it was before plus AI in in some way shape or
15:42
before plus AI in in some way shape or
15:42
before plus AI in in some way shape or form
15:44
form
15:44
form right maybe known as the cloud show plus
15:46
right maybe known as the cloud show plus
15:46
right maybe known as the cloud show plus AI maybe who knows Magnus I I don't know
15:49
AI maybe who knows Magnus I I don't know
15:49
AI maybe who knows Magnus I I don't know like we couldn't possibly fit AI into no
15:52
like we couldn't possibly fit AI into no
15:52
like we couldn't possibly fit AI into no I'm
15:54
I'm
15:54
I'm kidding all right so
15:57
kidding all right so
15:57
kidding all right so um one one thing that that is um like so
16:01
um one one thing that that is um like so
16:01
um one one thing that that is um like so important as well in this space is
16:03
important as well in this space is
16:03
important as well in this space is around uh how do you how do you take
16:05
around uh how do you how do you take
16:05
around uh how do you how do you take care of your AI solution and and how do
16:07
care of your AI solution and and how do
16:07
care of your AI solution and and how do you ensure that you are uh behaving
16:11
you ensure that you are uh behaving
16:11
you ensure that you are uh behaving fairly I guess the word is ethically um
16:14
fairly I guess the word is ethically um
16:14
fairly I guess the word is ethically um this is of course a huge um concern um
16:17
this is of course a huge um concern um
16:17
this is of course a huge um concern um so what does it take for an organization
16:19
so what does it take for an organization
16:19
so what does it take for an organization to ensure that they are being ethical in
16:23
to ensure that they are being ethical in
16:23
to ensure that they are being ethical in their AI approach and what does that
16:24
their AI approach and what does that
16:24
their AI approach and what does that mean even yeah great question because
16:27
mean even yeah great question because
16:27
mean even yeah great question because you're right it is very critical having
16:29
you're right it is very critical having
16:29
you're right it is very critical having a responsible AI if you would um
16:31
a responsible AI if you would um
16:31
a responsible AI if you would um responsible AI components such as
16:33
responsible AI components such as
16:33
responsible AI components such as fairness reliability and safety privacy
16:37
fairness reliability and safety privacy
16:37
fairness reliability and safety privacy and security right they're all critical
16:39
and security right they're all critical
16:39
and security right they're all critical components in the overall gen strategy
16:41
components in the overall gen strategy
16:41
components in the overall gen strategy so when I talk to our customers we
16:43
so when I talk to our customers we
16:44
so when I talk to our customers we that's always a topic that we bring up
16:46
that's always a topic that we bring up
16:46
that's always a topic that we bring up right away so but I mean Cloud vendors
16:49
right away so but I mean Cloud vendors
16:49
right away so but I mean Cloud vendors like Microsoft have built- in support
16:51
like Microsoft have built- in support
16:51
like Microsoft have built- in support for many of these fun key functionality
16:53
for many of these fun key functionality
16:53
for many of these fun key functionality out of the box and it's very
16:55
out of the box and it's very
16:55
out of the box and it's very configurable as well so if you want to
16:56
configurable as well so if you want to
16:56
configurable as well so if you want to configure it for your organization can
16:59
configure it for your organization can
16:59
configure it for your organization can an actually expert in the raai space as
17:02
an actually expert in the raai space as
17:02
an actually expert in the raai space as well so that so I I I recently published
17:05
well so that so I I I recently published
17:05
well so that so I I I recently published a Blog on this so I'm uh I would like to
17:08
a Blog on this so I'm uh I would like to
17:08
a Blog on this so I'm uh I would like to share that with the listeners later but
17:11
share that with the listeners later but
17:11
share that with the listeners later but so so yeah I mean like just like Paul
17:12
so so yeah I mean like just like Paul
17:12
so so yeah I mean like just like Paul said right starting with those
17:13
said right starting with those
17:13
said right starting with those principles early on thinking about all
17:17
principles early on thinking about all
17:17
principles early on thinking about all these privacy transparency
17:18
these privacy transparency
17:18
these privacy transparency accountability there are six principles
17:21
accountability there are six principles
17:21
accountability there are six principles that Microsoft recommends and thinking
17:23
that Microsoft recommends and thinking
17:23
that Microsoft recommends and thinking about the goals of your use case through
17:26
about the goals of your use case through
17:26
about the goals of your use case through the lens of these principles then as
17:29
the lens of these principles then as
17:29
the lens of these principles then as lishing an REI strategy through people
17:31
lishing an REI strategy through people
17:31
lishing an REI strategy through people process and Technology when I say people
17:33
process and Technology when I say people
17:33
process and Technology when I say people like having strong accountability
17:35
like having strong accountability
17:35
like having strong accountability systems a governance system like
17:38
systems a governance system like
17:38
systems a governance system like research policy engineering teams
17:39
research policy engineering teams
17:39
research policy engineering teams working together and then from the
17:41
working together and then from the
17:41
working together and then from the process standpoint having something like
17:43
process standpoint having something like
17:43
process standpoint having something like red teaming in your application uh
17:45
red teaming in your application uh
17:45
red teaming in your application uh development process so red teaming is
17:48
development process so red teaming is
17:48
development process so red teaming is proactively probing for risks in your
17:50
proactively probing for risks in your
17:50
proactively probing for risks in your application making sure uh they don't
17:54
application making sure uh they don't
17:54
application making sure uh they don't behave negatively right and then also
17:57
behave negatively right and then also
17:57
behave negatively right and then also designing metric RS U for risk and then
18:01
designing metric RS U for risk and then
18:01
designing metric RS U for risk and then measuring them at scale and using some
18:03
measuring them at scale and using some
18:03
measuring them at scale and using some tools like Azure content safety to
18:05
tools like Azure content safety to
18:05
tools like Azure content safety to mitigate those risks and then at the
18:07
mitigate those risks and then at the
18:07
mitigate those risks and then at the technology level uh like thinking about
18:10
technology level uh like thinking about
18:10
technology level uh like thinking about risk mitigation at every layer of
18:12
risk mitigation at every layer of
18:12
risk mitigation at every layer of Technology stack uh like one of them is
18:16
Technology stack uh like one of them is
18:16
Technology stack uh like one of them is having meta proms it's like a simple
18:18
having meta proms it's like a simple
18:18
having meta proms it's like a simple solution having like good well-defined
18:20
solution having like good well-defined
18:20
solution having like good well-defined meta proms that can you know improve the
18:23
meta proms that can you know improve the
18:23
meta proms that can you know improve the behavior of your models so things like
18:26
behavior of your models so things like
18:26
behavior of your models so things like that and also making sure you select
18:28
that and also making sure you select
18:28
that and also making sure you select models that adher to fairness and low
18:32
models that adher to fairness and low
18:32
models that adher to fairness and low toxicity and things like that there are
18:34
toxicity and things like that there are
18:34
toxicity and things like that there are various benchmarks that can help you
18:36
various benchmarks that can help you
18:36
various benchmarks that can help you give you that
18:37
give you that
18:37
give you that information okay yeah yeah that makes
18:39
information okay yeah yeah that makes
18:39
information okay yeah yeah that makes sense so tell me have you um you don't
18:41
sense so tell me have you um you don't
18:41
sense so tell me have you um you don't have to mention any any brand names
18:43
have to mention any any brand names
18:43
have to mention any any brand names because you can't uh but but have you
18:45
because you can't uh but but have you
18:46
because you can't uh but but have you found any uh customer case where they
18:48
found any uh customer case where they
18:48
found any uh customer case where they they discovered that they had a lot of
18:50
they discovered that they had a lot of
18:51
they discovered that they had a lot of data maybe but their data was there was
18:53
data maybe but their data was there was
18:53
data maybe but their data was there was bias in their data they had challenges
18:56
bias in their data they had challenges
18:56
bias in their data they had challenges with that have has that happened like is
18:57
with that have has that happened like is
18:57
with that have has that happened like is that a thing that you look at your own
19:00
that a thing that you look at your own
19:00
that a thing that you look at your own data and you just realize that oh wow I
19:02
data and you just realize that oh wow I
19:02
data and you just realize that oh wow I didn't know that that was in
19:05
didn't know that that was in
19:05
didn't know that that was in there yeah there is um primarily we see
19:08
there yeah there is um primarily we see
19:08
there yeah there is um primarily we see it in HR areas as well I won't go into
19:11
it in HR areas as well I won't go into
19:11
it in HR areas as well I won't go into details but no no no you can't go into
19:13
details but no no no you can't go into
19:13
details but no no no you can't go into details I'm not asking you but but but
19:15
details I'm not asking you but but but
19:15
details I'm not asking you but but but but effectively that right when you once
19:17
but effectively that right when you once
19:17
but effectively that right when you once you crunch all your data and and you
19:20
you crunch all your data and and you
19:20
you crunch all your data and and you start asking it questions y a bunch of
19:22
start asking it questions y a bunch of
19:22
start asking it questions y a bunch of really weird things come out and it's
19:24
really weird things come out and it's
19:24
really weird things come out and it's like what is that in my data yeah some
19:27
like what is that in my data yeah some
19:27
like what is that in my data yeah some of it is am yeah you know data is data
19:30
of it is am yeah you know data is data
19:30
of it is am yeah you know data is data and how you interpret is is just another
19:32
and how you interpret is is just another
19:32
and how you interpret is is just another layer yeah exactly yeah yeah that's
19:34
layer yeah exactly yeah yeah that's
19:35
layer yeah exactly yeah yeah that's interesting having that responsible AI
19:36
interesting having that responsible AI
19:36
interesting having that responsible AI layer is critical in every every
19:39
layer is critical in every every
19:39
layer is critical in every every deployment yes so so real like as a
19:42
deployment yes so so real like as a
19:42
deployment yes so so real like as a final thing here for this this quick
19:44
final thing here for this this quick
19:44
final thing here for this this quick conversation today um like can you give
19:46
conversation today um like can you give
19:46
conversation today um like can you give some advice on on like starting the
19:48
some advice on on like starting the
19:48
some advice on on like starting the cloud uh an AI generative AI Journey
19:51
cloud uh an AI generative AI Journey
19:51
cloud uh an AI generative AI Journey like what what does a company do like as
19:54
like what what does a company do like as
19:54
like what what does a company do like as as concise as possible if you can but
19:56
as concise as possible if you can but
19:56
as concise as possible if you can but you know what I mean
19:58
you know what I mean
19:59
you know what I mean go
19:59
go
19:59
go ah yeah so my advice would be to start
20:03
ah yeah so my advice would be to start
20:03
ah yeah so my advice would be to start with something like an ideation phase
20:05
with something like an ideation phase
20:05
with something like an ideation phase which I told you right like if they are
20:06
which I told you right like if they are
20:06
which I told you right like if they are starting now in generative and like
20:08
starting now in generative and like
20:08
starting now in generative and like doing hackathons which are a good
20:10
doing hackathons which are a good
20:10
doing hackathons which are a good breeding ground for like creative
20:11
breeding ground for like creative
20:11
breeding ground for like creative innovative solutions and then yep and
20:14
innovative solutions and then yep and
20:14
innovative solutions and then yep and then picking a lwh hanging fruit use
20:16
then picking a lwh hanging fruit use
20:16
then picking a lwh hanging fruit use case which could be like a simple
20:18
case which could be like a simple
20:18
case which could be like a simple internal co-pilot like an organizational
20:21
internal co-pilot like an organizational
20:21
internal co-pilot like an organizational chatbot a Rags solution right using
20:24
chatbot a Rags solution right using
20:24
chatbot a Rags solution right using Azure opener on your data something like
20:26
Azure opener on your data something like
20:26
Azure opener on your data something like that which will help you uh get to quick
20:28
that which will help you uh get to quick
20:28
that which will help you uh get to quick wins and and show value as well so and
20:32
wins and and show value as well so and
20:32
wins and and show value as well so and also get your hands dirty and your
20:34
also get your hands dirty and your
20:34
also get your hands dirty and your employees can learn about generative so
20:36
employees can learn about generative so
20:36
employees can learn about generative so that's the first thing and the second
20:38
that's the first thing and the second
20:38
that's the first thing and the second thing I would say is like when you're
20:39
thing I would say is like when you're
20:39
thing I would say is like when you're going to production thinking about
20:41
going to production thinking about
20:41
going to production thinking about latency and ux is very important that's
20:44
latency and ux is very important that's
20:44
latency and ux is very important that's what I have realized because uh if if uh
20:48
what I have realized because uh if if uh
20:48
what I have realized because uh if if uh it just takes one or two bad experiences
20:50
it just takes one or two bad experiences
20:50
it just takes one or two bad experiences for your users to switch the platform so
20:54
for your users to switch the platform so
20:54
for your users to switch the platform so latency and a good well-designed ux I
20:56
latency and a good well-designed ux I
20:56
latency and a good well-designed ux I think is our critical when you're going
20:58
think is our critical when you're going
20:58
think is our critical when you're going to
20:59
to
20:59
to production that makes sense so
21:01
production that makes sense so
21:01
production that makes sense so effectively you're saying like it's it's
21:03
effectively you're saying like it's it's
21:03
effectively you're saying like it's it's a step stepwise right start hacking get
21:06
a step stepwise right start hacking get
21:07
a step stepwise right start hacking get something anything that works U and and
21:09
something anything that works U and and
21:09
something anything that works U and and just feel it and like okay how did how
21:12
just feel it and like okay how did how
21:12
just feel it and like okay how did how did this feel like what is what is this
21:14
did this feel like what is what is this
21:14
did this feel like what is what is this about and then take the next step on top
21:16
about and then take the next step on top
21:16
about and then take the next step on top of that and as you mentioned Magnus it's
21:19
of that and as you mentioned Magnus it's
21:19
of that and as you mentioned Magnus it's a quick moving field like it's
21:21
a quick moving field like it's
21:21
a quick moving field like it's constantly changing so reading Vlogs or
21:23
constantly changing so reading Vlogs or
21:23
constantly changing so reading Vlogs or watching videos is also a great way to
21:25
watching videos is also a great way to
21:25
watching videos is also a great way to educate yourself fantastic well thank
21:28
educate yourself fantastic well thank
21:28
educate yourself fantastic well thank you you guys for being on the show today
21:29
you you guys for being on the show today
21:29
you you guys for being on the show today I I really appreciated this conversation
21:31
I I really appreciated this conversation
21:31
I I really appreciated this conversation and I think it clarified and and give
21:33
and I think it clarified and and give
21:33
and I think it clarified and and give some strategy and advice to a lot of of
21:35
some strategy and advice to a lot of of
21:35
some strategy and advice to a lot of of our our listeners so thank you guys for
21:38
our our listeners so thank you guys for
21:38
our our listeners so thank you guys for being on the cloud show thanks for
21:39
being on the cloud show thanks for
21:39
being on the cloud show thanks for having
21:40
having
21:40
having us yeah and uh for the audience I'll see
21:43
us yeah and uh for the audience I'll see
21:43
us yeah and uh for the audience I'll see you next time on the cloud show
21:46
you next time on the cloud show
21:46
you next time on the cloud show [Music]

The Cloud Show with Magnus Mårtensson ft. Anurag Karuparti & Paul S - Ep: 37

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The Cloud Show with Magnus Mårtensson ft. Anurag Karuparti & Paul S - Ep: 37

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