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AI in Retail: Developer Tools, APIs & MLOps Strategies
Sep 24, 2025
Retail is undergoing an AI-driven transformation — and developers are at the heart of it. This session explores the developer toolkit powering retail’s AI revolution, from advanced APIs and MLOps pipelines to recommendation frameworks and real-time personalization engines. Key insights covered: • Retail AI research grew from 12 papers (2000) to 847+ (2023) — a 21.3% CAGR • Modern AI systems process 150+ customer attributes in real time • Transformer-based models like BERT4Rec deliver 23–31% performance gains • Only 10% of AI implementations achieve ROI — we’ll uncover why • Generative AI in inventory systems processing 20–50 features •• Real-time personalization APIs achieving 75% accuracy • Forecasting improvements of 30–50% with scalable architectures Technical focus areas: • MLOps pipelines for scalable retail AI • API design patterns for recommendation engines • Data preprocessing tools for customer attribute management • Model deployment strategies for real-time personalization
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Hi everyone and welcome to a new episode
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Hi everyone and welcome to a new episode
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Hi everyone and welcome to a new episode of AI Dev Tools. In this episode, we're
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of AI Dev Tools. In this episode, we're
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of AI Dev Tools. In this episode, we're going to talk about DevTools API and
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going to talk about DevTools API and
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going to talk about DevTools API and implementation strategies on how you can
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implementation strategies on how you can
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implementation strategies on how you can build AIdriven retail applications. And
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build AIdriven retail applications. And
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build AIdriven retail applications. And joining us today is Mega who is uh
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joining us today is Mega who is uh
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joining us today is Mega who is uh senior program manager at Amazon. Hi
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senior program manager at Amazon. Hi
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senior program manager at Amazon. Hi Mega and welcome to the show.
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Mega and welcome to the show.
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Mega and welcome to the show. [Music]
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Hey, thank you so much. First of all,
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Hey, thank you so much. First of all,
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Hey, thank you so much. First of all, I'm super excited to be here and thanks
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I'm super excited to be here and thanks
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I'm super excited to be here and thanks for this opportunity to talk to you all
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for this opportunity to talk to you all
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for this opportunity to talk to you all about my experience. Um, as you
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about my experience. Um, as you
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about my experience. Um, as you mentioned, I'm Mega Huli. I'm a senior
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mentioned, I'm Mega Huli. I'm a senior
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mentioned, I'm Mega Huli. I'm a senior program manager at Amazon. Now, uh, my
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program manager at Amazon. Now, uh, my
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program manager at Amazon. Now, uh, my background essentially is I'm an
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background essentially is I'm an
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background essentially is I'm an engineer by education and I have a
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engineer by education and I have a
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engineer by education and I have a master's in business analytics with
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master's in business analytics with
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master's in business analytics with University of Texas at Dallas. In my
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University of Texas at Dallas. In my
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University of Texas at Dallas. In my journey, I took very different roles,
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journey, I took very different roles,
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journey, I took very different roles, business program, product manager roles
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business program, product manager roles
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business program, product manager roles where very in-depth of technical, right?
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where very in-depth of technical, right?
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where very in-depth of technical, right? as a technical product manager or a
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as a technical product manager or a
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as a technical product manager or a project manager and worked across many
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project manager and worked across many
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project manager and worked across many industries like healthcare e e-commerce
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industries like healthcare e e-commerce
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industries like healthcare e e-commerce uh warehouse automations or you know
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uh warehouse automations or you know
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uh warehouse automations or you know fuel pricing and many more industries as
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fuel pricing and many more industries as
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fuel pricing and many more industries as I've tried everything to make sure that
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I've tried everything to make sure that
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I've tried everything to make sure that we are um in a AI trends or provide the
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we are um in a AI trends or provide the
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we are um in a AI trends or provide the technical knowledge that I can provide
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technical knowledge that I can provide
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technical knowledge that I can provide and for me the common thread in all of
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and for me the common thread in all of
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and for me the common thread in all of them is technology and data I have the
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them is technology and data I have the
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them is technology and data I have the power to transfer business into improve
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power to transfer business into improve
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power to transfer business into improve the customer experience.
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the customer experience.
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the customer experience. Uh because I always the person that who
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Uh because I always the person that who
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Uh because I always the person that who love tackling challenges and hands on on
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love tackling challenges and hands on on
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love tackling challenges and hands on on picking new technology quickly and
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picking new technology quickly and
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picking new technology quickly and finding a ways to improve the process.
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finding a ways to improve the process.
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finding a ways to improve the process. That's about me. Don't want to take more
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That's about me. Don't want to take more
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That's about me. Don't want to take more time on talking about myself. Let's get
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time on talking about myself. Let's get
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time on talking about myself. Let's get started. So um I have spent my last
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started. So um I have spent my last
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started. So um I have spent my last decade working on intersection of
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decade working on intersection of
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decade working on intersection of AIdriven SAS platform, enterprise
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AIdriven SAS platform, enterprise
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AIdriven SAS platform, enterprise automation and product innovation. Much
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automation and product innovation. Much
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automation and product innovation. Much of my work has focused enabling cross
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of my work has focused enabling cross
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of my work has focused enabling cross functional teams from data scientist to
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functional teams from data scientist to
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functional teams from data scientist to product engineers to scale intelligence
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product engineers to scale intelligence
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product engineers to scale intelligence system that just don't work and truly
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system that just don't work and truly
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system that just don't work and truly deliver value which is very much needed
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deliver value which is very much needed
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deliver value which is very much needed right now. Today I'm super excited to
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right now. Today I'm super excited to
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right now. Today I'm super excited to walk you through technical journey of
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walk you through technical journey of
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walk you through technical journey of smart dev assistant AI agent agent mode
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smart dev assistant AI agent agent mode
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smart dev assistant AI agent agent mode in VS code with focus on how developers
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in VS code with focus on how developers
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in VS code with focus on how developers can lead retail AI transformation using
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can lead retail AI transformation using
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can lead retail AI transformation using robust schools uh robust skills uh agile
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robust schools uh robust skills uh agile
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robust schools uh robust skills uh agile workflows and scalable infrastructure.
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workflows and scalable infrastructure.
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workflows and scalable infrastructure. Okay, whether you are building
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Okay, whether you are building
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Okay, whether you are building recommendation engineer engineers or
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recommendation engineer engineers or
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recommendation engineer engineers or monitoring models in production or just
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monitoring models in production or just
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monitoring models in production or just getting started with MLOps.
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getting started with MLOps.
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getting started with MLOps. Hope this session will help you uh with
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Hope this session will help you uh with
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Hope this session will help you uh with anything that you need. Let's begin by
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anything that you need. Let's begin by
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anything that you need. Let's begin by looking at the behind the
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looking at the behind the
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looking at the behind the transformation. Retail AI development
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transformation. Retail AI development
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transformation. Retail AI development has grown drastically rate. Right? If we
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has grown drastically rate. Right? If we
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has grown drastically rate. Right? If we rewind to the early 2000, as you can see
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rewind to the early 2000, as you can see
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rewind to the early 2000, as you can see in the slide, it's just 12 AI research
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in the slide, it's just 12 AI research
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in the slide, it's just 12 AI research articles that were published and it was
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articles that were published and it was
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articles that were published and it was f it was focused basically on
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f it was focused basically on
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f it was focused basically on uh decision trees mostly rulebased
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uh decision trees mostly rulebased
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uh decision trees mostly rulebased systems for things like if user buys X
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systems for things like if user buys X
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systems for things like if user buys X then pro give them the show them the Y
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then pro give them the show them the Y
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then pro give them the show them the Y all right or if the user buy A and B
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all right or if the user buy A and B
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all right or if the user buy A and B then maybe give them the C. uh we never
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then maybe give them the C. uh we never
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then maybe give them the C. uh we never thought about during that time that if
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thought about during that time that if
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thought about during that time that if the user don't know what actually wants
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the user don't know what actually wants
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the user don't know what actually wants it from the data that we previously had
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it from the data that we previously had
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it from the data that we previously had it to show it but today in 2023 we are
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it to show it but today in 2023 we are
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it to show it but today in 2023 we are in the era of transforms and re
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in the era of transforms and re
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in the era of transforms and re reinformation of learning and GANs these
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reinformation of learning and GANs these
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reinformation of learning and GANs these models power personalized experience
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models power personalized experience
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models power personalized experience dynamic pricing even supply chain
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dynamic pricing even supply chain
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dynamic pricing even supply chain optimization
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optimization
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optimization consider this number of AI publication
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consider this number of AI publication
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consider this number of AI publication in retail jumped from few hundreds to
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in retail jumped from few hundreds to
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in retail jumped from few hundreds to 847 in 2023
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847 in 2023
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847 in 2023 uh driven largely by ML tools like
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uh driven largely by ML tools like
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uh driven largely by ML tools like TensorFlow, PI torch and open cloud
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TensorFlow, PI torch and open cloud
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TensorFlow, PI torch and open cloud platforms.
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platforms.
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platforms. At retail comp company right being from
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At retail comp company right being from
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At retail comp company right being from there for example we use deep learning
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there for example we use deep learning
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there for example we use deep learning for everything from anticipating
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for everything from anticipating
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for everything from anticipating customer demand to autogenerated item
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customer demand to autogenerated item
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customer demand to autogenerated item description. These are just from the
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description. These are just from the
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description. These are just from the scratch surface from the data that
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scratch surface from the data that
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scratch surface from the data that seeing what our user actually wants it
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seeing what our user actually wants it
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seeing what our user actually wants it it from not from one user right it from
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it from not from one user right it from
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it from not from one user right it from the uh millions of users that has been
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the uh millions of users that has been
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the uh millions of users that has been used on those description and how do
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used on those description and how do
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used on those description and how do they proceed it
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this search has brought into new kind of
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this search has brought into new kind of
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this search has brought into new kind of architecture right the modern retail AI
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architecture right the modern retail AI
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architecture right the modern retail AI systems are built on a foundation of
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systems are built on a foundation of
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systems are built on a foundation of realtime data and microservices Picture
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realtime data and microservices Picture
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realtime data and microservices Picture a system that analyzed 150 customer
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a system that analyzed 150 customer
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a system that analyzed 150 customer attributes in a real time things like
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attributes in a real time things like
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attributes in a real time things like preferred price point click uh clicking
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preferred price point click uh clicking
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preferred price point click uh clicking bound behavior or even device types
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bound behavior or even device types
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bound behavior or even device types right recent one of the examples that
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right recent one of the examples that
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right recent one of the examples that I've seen uh that right even if the
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I've seen uh that right even if the
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I've seen uh that right even if the people are buying something from Android
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people are buying something from Android
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people are buying something from Android the prices were very change compared to
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the prices were very change compared to
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the prices were very change compared to the Apple devices same way there's a
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the Apple devices same way there's a
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the Apple devices same way there's a trend that has been observed from our
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trend that has been observed from our
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trend that has been observed from our users that what kind of a device user
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users that what kind of a device user
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users that what kind of a device user what do they use it on the development
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what do they use it on the development
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what do they use it on the development side nearly 70% of the retail AI
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side nearly 70% of the retail AI
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side nearly 70% of the retail AI research today on deep learning we use
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research today on deep learning we use
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research today on deep learning we use frameworks like bird fork which applies
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frameworks like bird fork which applies
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frameworks like bird fork which applies transform based model to understand user
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transform based model to understand user
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transform based model to understand user user browsing sequence this along can
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user browsing sequence this along can
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user browsing sequence this along can lead into 23 to 30% improvement in
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lead into 23 to 30% improvement in
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lead into 23 to 30% improvement in recommendation accuracy
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recommendation accuracy
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recommendation accuracy uh one of The recent papers that I've
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uh one of The recent papers that I've
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uh one of The recent papers that I've read about is the
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read about is the
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read about is the major beauty retail industry
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major beauty retail industry
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major beauty retail industry that is using bird based model on the
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that is using bird based model on the
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that is using bird based model on the personalized homepage layout based on
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personalized homepage layout based on
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personalized homepage layout based on the recent research and research terms
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the recent research and research terms
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the recent research and research terms and uh pre purchase which they have seen
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and uh pre purchase which they have seen
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and uh pre purchase which they have seen 22% lith on the clickthrough rates right
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22% lith on the clickthrough rates right
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22% lith on the clickthrough rates right for an user it is very for an app it is
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for an user it is very for an app it is
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for an user it is very for an app it is very important that the way the users
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very important that the way the users
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very important that the way the users are clicking the small models on AI uh
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are clicking the small models on AI uh
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are clicking the small models on AI uh has can change the business altogether.
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has can change the business altogether.
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has can change the business altogether. Okay, let's take a moment to pause here.
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Okay, let's take a moment to pause here.
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Okay, let's take a moment to pause here. Um despite of all this innovation, only
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Um despite of all this innovation, only
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Um despite of all this innovation, only one out of 10 AI development in retail
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one out of 10 AI development in retail
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one out of 10 AI development in retail leads to a real business ROI. Uh this is
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leads to a real business ROI. Uh this is
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leads to a real business ROI. Uh this is pretty surprising whenever we hear the
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pretty surprising whenever we hear the
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pretty surprising whenever we hear the number for the first time. But let's
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number for the first time. But let's
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number for the first time. But let's look why the technical challenges go
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look why the technical challenges go
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look why the technical challenges go above beyond the tuning right we often
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above beyond the tuning right we often
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above beyond the tuning right we often see data pipelining that aren't
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see data pipelining that aren't
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see data pipelining that aren't production ready no food feedback loop
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production ready no food feedback loop
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production ready no food feedback loop for model drift or API that were built
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for model drift or API that were built
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for model drift or API that were built with thought clear consumption strategy
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with thought clear consumption strategy
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with thought clear consumption strategy that's when the lot of product and
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that's when the lot of product and
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that's when the lot of product and program managers and business analyst or
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program managers and business analyst or
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program managers and business analyst or a business leader comes into picture to
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a business leader comes into picture to
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a business leader comes into picture to drive this AI really
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drive this AI really
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drive this AI really for for retail company you know train to
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for for retail company you know train to
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for for retail company you know train to create a right recommendation model but
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create a right recommendation model but
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create a right recommendation model but it wasn't just used because of a front-
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it wasn't just used because of a front-
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it wasn't just used because of a front- end app team right it has to be easy
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end app team right it has to be easy
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end app team right it has to be easy with integrations with API as well now
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with integrations with API as well now
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with integrations with API as well now let's look at practical approach to
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let's look at practical approach to
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let's look at practical approach to making this work in production we saw
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making this work in production we saw
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making this work in production we saw that how one out of 10 are uh the AI
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that how one out of 10 are uh the AI
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that how one out of 10 are uh the AI development tools uh were only working
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development tools uh were only working
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development tools uh were only working So looking at the approach
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So looking at the approach
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So looking at the approach generative AR for forecasting think
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generative AR for forecasting think
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generative AR for forecasting think about inventory system retailers deal
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about inventory system retailers deal
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about inventory system retailers deal with 20 to 50 features like historical
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with 20 to 50 features like historical
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with 20 to 50 features like historical sales holidays even weather.
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sales holidays even weather.
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sales holidays even weather. With LSTM and transformers we can
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With LSTM and transformers we can
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With LSTM and transformers we can generate forwardlooking insight to
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generate forwardlooking insight to
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generate forwardlooking insight to predict what SK used to restock in what
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predict what SK used to restock in what
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predict what SK used to restock in what quantity right and when. For example, um
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quantity right and when. For example, um
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quantity right and when. For example, um some of the big industry like Target or
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some of the big industry like Target or
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some of the big industry like Target or Walmart or including Amazon user demands
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Walmart or including Amazon user demands
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Walmart or including Amazon user demands forecasting model that adjust inventory
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forecasting model that adjust inventory
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forecasting model that adjust inventory in a real time because we just can't
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in a real time because we just can't
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in a real time because we just can't have all the stuffs in the inventory on
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have all the stuffs in the inventory on
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have all the stuffs in the inventory on the warehouse and then we don't know
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the warehouse and then we don't know
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the warehouse and then we don't know when the user going to buy it right so
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when the user going to buy it right so
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when the user going to buy it right so it's important for us to forecast major
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it's important for us to forecast major
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it's important for us to forecast major events like back to school or holiday
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events like back to school or holiday
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events like back to school or holiday season or uh black Friday or Christmas
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season or uh black Friday or Christmas
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season or uh black Friday or Christmas events right So it is well in advance
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events right So it is well in advance
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events right So it is well in advance maybe a month prior or two months prior
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maybe a month prior or two months prior
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maybe a month prior or two months prior the the team start working on to see
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the the team start working on to see
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the the team start working on to see that what the forecasting will look for
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that what the forecasting will look for
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that what the forecasting will look for these event uh real time personalization
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these event uh real time personalization
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these event uh real time personalization archiving 75% of accuracy in
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archiving 75% of accuracy in
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archiving 75% of accuracy in personalized means optimizing for speed
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personalized means optimizing for speed
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personalized means optimizing for speed like using Kafka or microservices or
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like using Kafka or microservices or
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like using Kafka or microservices or retail process user click instantly
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retail process user click instantly
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retail process user click instantly recommend next best offer under 100
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recommend next best offer under 100
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recommend next best offer under 100 milliseconds, right? Um, one of the
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milliseconds, right? Um, one of the
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milliseconds, right? Um, one of the fashion uh conversation rate jump eight
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fashion uh conversation rate jump eight
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fashion uh conversation rate jump eight 18% simply by switching formulas which
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18% simply by switching formulas which
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18% simply by switching formulas which I've seen some of the retails like TU or
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I've seen some of the retails like TU or
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I've seen some of the retails like TU or um anywhere that you see a lot of
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um anywhere that you see a lot of
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um anywhere that you see a lot of discounts that they're putting in
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discounts that they're putting in
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discounts that they're putting in because you just click through it and
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because you just click through it and
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because you just click through it and they know that they want you to buy it.
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they know that they want you to buy it.
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they know that they want you to buy it. So they uh make sure that you know put
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So they uh make sure that you know put
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So they uh make sure that you know put with a nice offer. So this is how we do
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with a nice offer. So this is how we do
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with a nice offer. So this is how we do it in the real world and scalable
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it in the real world and scalable
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it in the real world and scalable architectures using Kubernetes. You can
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architectures using Kubernetes. You can
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architectures using Kubernetes. You can horizontally scale workloads during peak
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horizontally scale workloads during peak
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horizontally scale workloads during peak scales events like as I said black
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scales events like as I said black
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scales events like as I said black Friday at
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Friday at
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Friday at we built internal tools that the scale
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we built internal tools that the scale
9:42
we built internal tools that the scale models serving up and down based on the
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models serving up and down based on the
9:46
models serving up and down based on the requested volume to ensure consistency.
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requested volume to ensure consistency.
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requested volume to ensure consistency. Let us see without overpromising. We
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Let us see without overpromising. We
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Let us see without overpromising. We have seen in the last few years before
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have seen in the last few years before
9:54
have seen in the last few years before the pages used to load more when there's
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the pages used to load more when there's
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the pages used to load more when there's an offer we're not able to click or we
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an offer we're not able to click or we
10:00
an offer we're not able to click or we have seen that how do I proceed further
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have seen that how do I proceed further
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have seen that how do I proceed further but currently because uh thanks to IML
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but currently because uh thanks to IML
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but currently because uh thanks to IML model that you can see because of
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model that you can see because of
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model that you can see because of Kubernetes
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Kubernetes
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Kubernetes uh with the current industry that we
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uh with the current industry that we
10:12
uh with the current industry that we have it loads faster and we see the
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have it loads faster and we see the
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have it loads faster and we see the results faster.
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results faster.
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results faster. ML ops pipelines for retail. Let's
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ML ops pipelines for retail. Let's
10:19
ML ops pipelines for retail. Let's really zoom into it to see the backbone
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really zoom into it to see the backbone
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really zoom into it to see the backbone that powers all this. Right? We started
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that powers all this. Right? We started
10:26
that powers all this. Right? We started with data collection and ETL pulling
10:29
with data collection and ETL pulling
10:29
with data collection and ETL pulling customer and product data into feature
10:31
customer and product data into feature
10:31
customer and product data into feature stories.
10:33
stories.
10:33
stories. Right? It's important that what we are
10:35
Right? It's important that what we are
10:35
Right? It's important that what we are building if you're not making into
10:36
building if you're not making into
10:36
building if you're not making into feature just we are building something
10:39
feature just we are building something
10:39
feature just we are building something then if the requirements are not written
10:42
then if the requirements are not written
10:42
then if the requirements are not written well then we cannot proceed. So it's
10:44
well then we cannot proceed. So it's
10:44
well then we cannot proceed. So it's important the data collections and
10:47
important the data collections and
10:47
important the data collections and processing in the ETL CI/CD plannings
10:51
processing in the ETL CI/CD plannings
10:51
processing in the ETL CI/CD plannings allow each model interaction to go
10:54
allow each model interaction to go
10:54
allow each model interaction to go through validations right it's important
10:57
through validations right it's important
10:57
through validations right it's important that we have a pipeline so you're not
10:59
that we have a pipeline so you're not
10:59
that we have a pipeline so you're not doing anything manually being in the uh
11:02
doing anything manually being in the uh
11:02
doing anything manually being in the uh AI world now so using like tensorflow pi
11:05
AI world now so using like tensorflow pi
11:06
AI world now so using like tensorflow pi torch and ab testing it's important that
11:09
torch and ab testing it's important that
11:09
torch and ab testing it's important that how how much the talk is happening with
11:11
how how much the talk is happening with
11:11
how how much the talk is happening with AB testing we make sure that you know
11:14
AB testing we make sure that you know
11:14
AB testing we make sure that you know giving different insight to our users to
11:17
giving different insight to our users to
11:17
giving different insight to our users to see that what they exactly wanted. Uh do
11:20
see that what they exactly wanted. Uh do
11:20
see that what they exactly wanted. Uh do you like the button to be blue, black,
11:23
you like the button to be blue, black,
11:23
you like the button to be blue, black, red, green? So giving all that to the
11:25
red, green? So giving all that to the
11:25
red, green? So giving all that to the different users to see that who is
11:28
different users to see that who is
11:28
different users to see that who is clicking which color is also is driven
11:30
clicking which color is also is driven
11:30
clicking which color is also is driven by AI. That's when the technology is
11:33
by AI. That's when the technology is
11:33
by AI. That's when the technology is going yeah like Docker and Kubernetes
11:36
going yeah like Docker and Kubernetes
11:36
going yeah like Docker and Kubernetes help to push these models into
11:38
help to push these models into
11:38
help to push these models into production at scale. A system like this
11:41
production at scale. A system like this
11:41
production at scale. A system like this monitors retain on drift and keeps the
11:43
monitors retain on drift and keeps the
11:43
monitors retain on drift and keeps the performance on check which is the beauty
11:46
performance on check which is the beauty
11:46
performance on check which is the beauty of the MLOps pipelines.
11:50
of the MLOps pipelines.
11:50
of the MLOps pipelines. Moving on. Now let's talk about the API
11:52
Moving on. Now let's talk about the API
11:52
Moving on. Now let's talk about the API because they they are where your models
11:56
because they they are where your models
11:56
because they they are where your models meet the user, right? You the
11:58
meet the user, right? You the
11:58
meet the user, right? You the interaction between the API um and the
12:01
interaction between the API um and the
12:01
interaction between the API um and the UI which really shows you that what
12:04
UI which really shows you that what
12:04
UI which really shows you that what exactly it is. Great APIs are faster,
12:08
exactly it is. Great APIs are faster,
12:08
exactly it is. Great APIs are faster, secures and flexible. REST is great for
12:11
secures and flexible. REST is great for
12:11
secures and flexible. REST is great for simple product calls. You know, GraphQL
12:14
simple product calls. You know, GraphQL
12:14
simple product calls. You know, GraphQL is a perfect perfect for nested
12:17
is a perfect perfect for nested
12:17
is a perfect perfect for nested structures like outfit
12:20
structures like outfit
12:20
structures like outfit gre ideas for internal high-speed micros
12:22
gre ideas for internal high-speed micros
12:22
gre ideas for internal high-speed micros service communication.
12:24
service communication.
12:24
service communication. But it's important don't not it does not
12:26
But it's important don't not it does not
12:26
But it's important don't not it does not forget catching authentication and rate
12:30
forget catching authentication and rate
12:30
forget catching authentication and rate limiting of all which impact the
12:32
limiting of all which impact the
12:32
limiting of all which impact the customer experience. So keeping the
12:35
customer experience. So keeping the
12:35
customer experience. So keeping the security privacy of a user with that
12:39
security privacy of a user with that
12:39
security privacy of a user with that giving them the faster and more accurate
12:43
giving them the faster and more accurate
12:43
giving them the faster and more accurate system is important. So APIs plays a
12:46
system is important. So APIs plays a
12:46
system is important. So APIs plays a very major role in that. So considering
12:48
very major role in that. So considering
12:48
very major role in that. So considering this is a one of the major major things
12:51
this is a one of the major major things
12:51
this is a one of the major major things for any of the product development
12:55
for any of the product development
12:55
for any of the product development before any model is trained the
12:57
before any model is trained the
12:57
before any model is trained the real-time battle is won or lost the data
13:00
real-time battle is won or lost the data
13:00
real-time battle is won or lost the data prep-processing
13:02
prep-processing
13:02
prep-processing use feature tools to automate feature
13:05
use feature tools to automate feature
13:05
use feature tools to automate feature engineering or DQ validation on the
13:08
engineering or DQ validation on the
13:08
engineering or DQ validation on the large scale attribution sets embedding
13:11
large scale attribution sets embedding
13:11
large scale attribution sets embedding tools
13:13
tools
13:13
tools transforming logs into dense vector that
13:16
transforming logs into dense vector that
13:16
transforming logs into dense vector that boost the like quality to 15 to 20%.
13:21
boost the like quality to 15 to 20%.
13:21
boost the like quality to 15 to 20%. As I've given an example of Kafka, the
13:24
As I've given an example of Kafka, the
13:24
As I've given an example of Kafka, the Kafka streams allows real-time feature
13:27
Kafka streams allows real-time feature
13:27
Kafka streams allows real-time feature updates every interaction, you know,
13:29
updates every interaction, you know,
13:29
updates every interaction, you know, increasing the personalization once you
13:32
increasing the personalization once you
13:32
increasing the personalization once you click on if you go to amazon.com if you
13:35
click on if you go to amazon.com if you
13:35
click on if you go to amazon.com if you click on the pencil, it also give the
13:38
click on the pencil, it also give the
13:38
click on the pencil, it also give the real-time examples of the other pencils
13:40
real-time examples of the other pencils
13:40
real-time examples of the other pencils that you can buy it from, right? or the
13:42
that you can buy it from, right? or the
13:42
that you can buy it from, right? or the fancier ones or which you never thought
13:45
fancier ones or which you never thought
13:45
fancier ones or which you never thought that is there in the market right now.
13:47
that is there in the market right now.
13:47
that is there in the market right now. So that in the back end maybe Kafka or
13:51
So that in the back end maybe Kafka or
13:51
So that in the back end maybe Kafka or active MQ or some of the processing are
13:54
active MQ or some of the processing are
13:54
active MQ or some of the processing are helping to make the system smarter.
13:58
Model strategy for you know real time
14:01
Model strategy for you know real time
14:01
Model strategy for you know real time personalization your development
14:03
personalization your development
14:03
personalization your development strategy must fit your model's purpose.
14:06
strategy must fit your model's purpose.
14:06
strategy must fit your model's purpose. If you're making something and it is not
14:08
If you're making something and it is not
14:08
If you're making something and it is not reaching the purpose and there's no
14:10
reaching the purpose and there's no
14:10
reaching the purpose and there's no point of having that. Uh some of them
14:12
point of having that. Uh some of them
14:12
point of having that. Uh some of them are tensorflow serving or onx real time
14:15
are tensorflow serving or onx real time
14:15
are tensorflow serving or onx real time help with the interface. Docker
14:18
help with the interface. Docker
14:18
help with the interface. Docker containers provide um reproductibility
14:22
containers provide um reproductibility
14:22
containers provide um reproductibility right tensorflow's light on edge devices
14:25
right tensorflow's light on edge devices
14:26
right tensorflow's light on edge devices can reduce latency up to 60% of a
14:28
can reduce latency up to 60% of a
14:28
can reduce latency up to 60% of a realtime in store interaction. And of
14:31
realtime in store interaction. And of
14:31
realtime in store interaction. And of course, AB testing lets you trial
14:33
course, AB testing lets you trial
14:33
course, AB testing lets you trial multiple versions automatically roll
14:36
multiple versions automatically roll
14:36
multiple versions automatically roll back when it is needed. Um, again going
14:39
back when it is needed. Um, again going
14:39
back when it is needed. Um, again going back to the same example that I was
14:41
back to the same example that I was
14:41
back to the same example that I was going on the different colors of the
14:43
going on the different colors of the
14:43
going on the different colors of the buttons, the AB testing would see that
14:46
buttons, the AB testing would see that
14:46
buttons, the AB testing would see that uh if the users are buying if the button
14:48
uh if the users are buying if the button
14:48
uh if the users are buying if the button is more of a green because they feel it
14:50
is more of a green because they feel it
14:50
is more of a green because they feel it is more relatable or the lighter color.
14:53
is more relatable or the lighter color.
14:53
is more relatable or the lighter color. So then we can switch behind for the
14:55
So then we can switch behind for the
14:55
So then we can switch behind for the people that who had a red and giving out
14:56
people that who had a red and giving out
14:56
people that who had a red and giving out the green will they click through it. So
14:59
the green will they click through it. So
14:59
the green will they click through it. So those kind of a testing really helps
15:00
those kind of a testing really helps
15:00
those kind of a testing really helps here.
15:03
here.
15:03
here. Monitoring framework let's not forget
15:06
Monitoring framework let's not forget
15:06
Monitoring framework let's not forget this metrics. Um on this technical side
15:10
this metrics. Um on this technical side
15:10
this metrics. Um on this technical side track frictions recall latency
15:13
track frictions recall latency
15:13
track frictions recall latency throughout drift and system health. On
15:16
throughout drift and system health. On
15:16
throughout drift and system health. On this business side tie your models with
15:18
this business side tie your models with
15:18
this business side tie your models with the key KPIs is what always I say and
15:21
the key KPIs is what always I say and
15:21
the key KPIs is what always I say and important to know what is the
15:22
important to know what is the
15:22
important to know what is the conversation rate are what is the order
15:25
conversation rate are what is the order
15:25
conversation rate are what is the order value revenue and the engagement.
15:28
value revenue and the engagement.
15:28
value revenue and the engagement. As a TPM, I track revenue attribution
15:31
As a TPM, I track revenue attribution
15:31
As a TPM, I track revenue attribution from AB test to ensure value delivery.
15:34
from AB test to ensure value delivery.
15:34
from AB test to ensure value delivery. If we don't have the value for the
15:36
If we don't have the value for the
15:36
If we don't have the value for the delivery, then there's no point of
15:38
delivery, then there's no point of
15:38
delivery, then there's no point of having it, right? Just because we have a
15:42
having it, right? Just because we have a
15:42
having it, right? Just because we have a time and we want to show it to make sure
15:44
time and we want to show it to make sure
15:44
time and we want to show it to make sure that it is automated. But if it is
15:46
that it is automated. But if it is
15:46
that it is automated. But if it is taking more time than the general it is,
15:48
taking more time than the general it is,
15:48
taking more time than the general it is, then there's no point of it. So, it's
15:50
then there's no point of it. So, it's
15:50
then there's no point of it. So, it's very very very important to have uh tie
15:53
very very very important to have uh tie
15:53
very very very important to have uh tie the models with key KPIs.
15:59
Okay. So I put this one on the last to
16:02
Okay. So I put this one on the last to
16:02
Okay. So I put this one on the last to see that how key to successful AI
16:04
see that how key to successful AI
16:04
see that how key to successful AI implementation work. So how do we ensure
16:07
implementation work. So how do we ensure
16:07
implementation work. So how do we ensure long-term success right? That is one of
16:09
long-term success right? That is one of
16:09
long-term success right? That is one of the things that we always ask anything
16:11
the things that we always ask anything
16:11
the things that we always ask anything that we build. First of all very
16:14
that we build. First of all very
16:14
that we build. First of all very important build across functional teams
16:16
important build across functional teams
16:16
important build across functional teams align engineers scientist and analysts
16:19
align engineers scientist and analysts
16:19
align engineers scientist and analysts from the start from the beginning that
16:21
from the start from the beginning that
16:21
from the start from the beginning that you thought you start going to build the
16:24
you thought you start going to build the
16:24
you thought you start going to build the some of the AI tools or internal
16:26
some of the AI tools or internal
16:26
some of the AI tools or internal external or you want to link it to the
16:28
external or you want to link it to the
16:28
external or you want to link it to the APIs anything that we are building get
16:30
APIs anything that we are building get
16:30
APIs anything that we are building get together everybody make sure everyone is
16:33
together everybody make sure everyone is
16:33
together everybody make sure everyone is on the same plate
16:35
on the same plate
16:35
on the same plate and use CI/CD and AB testing to release
16:40
and use CI/CD and AB testing to release
16:40
and use CI/CD and AB testing to release models faster. So we're not taking we're
16:42
models faster. So we're not taking we're
16:42
models faster. So we're not taking we're in the agile world. Uh it is not like a
16:44
in the agile world. Uh it is not like a
16:44
in the agile world. Uh it is not like a waterfall that we used to do before. So
16:47
waterfall that we used to do before. So
16:47
waterfall that we used to do before. So make sure that you're going fast and
16:49
make sure that you're going fast and
16:49
make sure that you're going fast and depending on the AB testing trial with
16:51
depending on the AB testing trial with
16:51
depending on the AB testing trial with users. Do not give the full-fledged
16:53
users. Do not give the full-fledged
16:53
users. Do not give the full-fledged which we are not sure that whether our
16:56
which we are not sure that whether our
16:56
which we are not sure that whether our users will like it or not. Customize
16:58
users will like it or not. Customize
16:58
users will like it or not. Customize everything and prioritize data quality
17:02
everything and prioritize data quality
17:02
everything and prioritize data quality is governance, validation and feature
17:04
is governance, validation and feature
17:04
is governance, validation and feature stories. Make sure the data is right.
17:07
stories. Make sure the data is right.
17:08
stories. Make sure the data is right. feed more data to the system as much as
17:10
feed more data to the system as much as
17:10
feed more data to the system as much as you can. Run on internal uh data for the
17:14
you can. Run on internal uh data for the
17:14
you can. Run on internal uh data for the for the company that we have uh or any
17:17
for the company that we have uh or any
17:17
for the company that we have uh or any data which is available for you. Feed
17:19
data which is available for you. Feed
17:19
data which is available for you. Feed it, try it, validate it really well and
17:23
it, try it, validate it really well and
17:23
it, try it, validate it really well and make sure the feature stories are
17:24
make sure the feature stories are
17:24
make sure the feature stories are aligned with ROI.
17:26
aligned with ROI.
17:26
aligned with ROI. Set a success metrics. Track both
17:29
Set a success metrics. Track both
17:29
Set a success metrics. Track both technical and business performance. Make
17:31
technical and business performance. Make
17:31
technical and business performance. Make sure that you have everything needed to
17:34
sure that you have everything needed to
17:34
sure that you have everything needed to call it as whether this project
17:35
call it as whether this project
17:35
call it as whether this project successful in the EB testing. If it is
17:38
successful in the EB testing. If it is
17:38
successful in the EB testing. If it is not, it's okay to move on then getting
17:40
not, it's okay to move on then getting
17:40
not, it's okay to move on then getting it stacks.
17:42
it stacks.
17:42
it stacks. Okay, this closes the loop between dev
17:44
Okay, this closes the loop between dev
17:44
Okay, this closes the loop between dev work and ROI that how the world has
17:47
work and ROI that how the world has
17:47
work and ROI that how the world has drastically changed for us.
17:50
drastically changed for us.
17:50
drastically changed for us. Yes, to finally to wrap up, AI is not
17:53
Yes, to finally to wrap up, AI is not
17:53
Yes, to finally to wrap up, AI is not just about building smart models. It's
17:55
just about building smart models. It's
17:55
just about building smart models. It's about building systems ones that scale,
17:59
about building systems ones that scale,
17:59
about building systems ones that scale, adopt and deliver real business value.
18:02
adopt and deliver real business value.
18:02
adopt and deliver real business value. The smart divi dev assistant in VS codes
18:05
The smart divi dev assistant in VS codes
18:05
The smart divi dev assistant in VS codes bring development, testing, deployment
18:08
bring development, testing, deployment
18:08
bring development, testing, deployment and monitoring into the intelligent
18:11
and monitoring into the intelligent
18:11
and monitoring into the intelligent workflow which is very important. It's
18:13
workflow which is very important. It's
18:13
workflow which is very important. It's not that what we build, what we maintain
18:16
not that what we build, what we maintain
18:16
not that what we build, what we maintain it, what we monitor it.
18:18
it, what we monitor it.
18:18
it, what we monitor it. I think that's all I had. Thank you so
18:20
I think that's all I had. Thank you so
18:20
I think that's all I had. Thank you so much for your
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