Machine Learning and Sagemaker at Zalando Mark¢  Machine Learning and Sagemaker at Zalando...

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Transcript of Machine Learning and Sagemaker at Zalando Mark¢  Machine Learning and Sagemaker at Zalando...

  • Kshitij Kumar, VP Data Infrastructure Zalando SE Kshitij.Kumar@zalando.de

    Machine Learning and Sagemaker at Zalando

    Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de

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    WE LOVE FASHION

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    WHAT STARTED AS A SIMPLE ONLINE SHOP…

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    …HAS BECOME THE EUROPEAN ONLINE PLATFORM FOR FASHION

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    WE OFFER A SUCCESSFUL AND CURATED ASSORTMENT

    > 300,000 articles from

    ~ 2,000 international brands

    15 privatelabels

    HIGHLY EXPERIENCED category management

    > 500 designers & stylistsLOCALIZATION

    of the assortment

    CURATED SHOPPING

    with Zalon

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    PLATFORM STRATEGY

    BRANDS CONSUMERS

    ENABLER

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    WE DRESS CODE

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    WE ARE CONSTANTLY INNOVATING

    CLOUD-BASED, CUTTING-EDGE & SCALABLE technology solutions

    > 2,000 employees at

    international tech locations8

    HQs in Berlin

    help our brand to WIN ONLINE

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    Put images in the grey dotted box "unsupported placeholder"Possible use cases of ML at an online retailer

  • An ML Driven Customer

    Experience

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  • ML driven real-time

    recommendation engine

    People who browsed this style also browsed these other styles…

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  • Complete the look

    Multi-dimensional ML driven product placement

    Search

    Recommended products

    Complimentary items

    Size (fit)

    Delivery promise

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  • ML Driven Supply Chain Management

    What? ❏ Do we need to provide?

    When? ❏ Do we need to provide it?

    Where? ❏ Should it be available?

    How much?

    ❏ Should it be available?

  • We use a myriad of tools

    Nakadi

  • The ML JourneyDigital Foundation - Data

    Explore

    Fetch

    Prepare

    Train Model

    Evaluate Model

    Deploy to production

    Monitor/ Evaluate Ready the Data

    Prepare the models

    Serve the models

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    Achieving the balance to run ML at Scale

    Exploding new With the needs

    Speed of Experimentation

    Safe environment with metadata

    Cost Efficiency

    Number of User teams

    Use cases

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    The ML pipeline – for a single use case

    ML Use Case Notebook/UI

    creates workflows

    Fetch Data

    Extract Features

    Prepare Data

    Train Model Deploy Model

    Serve

    Monitor

    Evaluate and Feedback

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    The ML pipeline – a couple of use cases

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    The ML pipeline – many use cases

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    Why SageMaker at Zalando

    ML at scale, with cost efficiency

    The ability to run hundreds of training jobs that are “serverless”. Trainings produce models and infrastructure is automatically shutdown.

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    ML at scale, with safety

    The ability to understand metadata at every stage of the ML journey by just describing a training job at the call of

    an API.

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    ML at scale, with speed

    The ability to compose training jobs, tuning jobs and endpoints with ease, at the call of an API, and with algorithms available out of the box.

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  • Productionizing ML Pipelines At Zalando

    (Speed, Safety, Cost Efficiency)

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    An end to end pipeline in action

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    An end to end pipeline in action

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    Productionizing ML: Speed, with simplicity

    src/lambdas/training_job.py src/lambdas/endpoint.py cf.yaml ci-cd.yaml

    CF: 1. Step functions definition 2. Trigger rule 3. Roles

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    Productionizing ML: Speed, with simplicity

    Container / script

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    Productionizing ML: Speed, with simplicity

    {experiment_id_ts}-{build_number}

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    Productionizing ML: Speed, with simplicity

    {experiment_id_ts}-{build_number}

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    Productionizing ML: Speed, with simplicity

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    Productionizing ML: Speed, with cost efficiency

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    ML pipelines should be safe and understandable

    Did it run properly?

    How many times did the pipeline run?

    When?

    Who has permissions to run the pipeline?

    When was the pipeline created?

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    ML pipelines should be safe and understandable

    What happened in each step of the pipeline?

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    ML pipelines should be safe and understandable

    How long did it run?

    When did it run?

    Name?

    Who had permissions to run it?

    Did it run properly?

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    ML pipelines should be safe and understandable

    What algorithm was used?

    What did it run on?

    How was the data loaded?

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    ML pipelines should be safe and understandable

    What exact data was used for training?

    What exact data was used for testing?

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    ML pipelines should be safe and understandable

    How was the training monitored

    What parameters were fed to the model

    Where was the output model stored

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    ML pipelines should be safe and understandable

    How did the training progress?

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    ML pipelines should be safe and understandable

    Where is the model deployed?

    When was the model deployed?

    Is the model in use?

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    ML pipelines should be safe and understandable

    What training job resulted in the deployment? Which model(s) was deployed?

    What instances are the model(s) deployed?

    How much tra