machine learning and sagemaker at zalando mark… · machine learning and sagemaker at zalando...

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Kshitij Kumar, VP Data Infrastructure Zalando SE [email protected] Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE [email protected]

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Page 1: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

Kshitij Kumar,VP Data InfrastructureZalando [email protected]

Machine Learning and Sagemaker at Zalando

Suhas GuruprasadML Engineering LeadZalando [email protected]

Page 2: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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WE LOVE FASHION

Page 3: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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WHAT STARTED AS A SIMPLE ONLINE SHOP…

Page 4: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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…HAS BECOME THE EUROPEAN ONLINE PLATFORMFOR FASHION

Page 5: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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WE OFFER A SUCCESSFUL AND CURATED ASSORTMENT

> 300,000articles from

~ 2,000international brands

15 privatelabels

HIGHLYEXPERIENCEDcategory management

> 500designers& stylistsLOCALIZATION

of the assortment

CURATEDSHOPPING

with Zalon

Page 6: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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PLATFORM STRATEGY

BRANDS CONSUMERS

ENABLER

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WE DRESS CODE

Page 8: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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WE ARE CONSTANTLY INNOVATING

CLOUD-BASED,CUTTING-EDGE& SCALABLEtechnology solutions

> 2,000employees at

international tech locations8

HQsin Berlin

help our brand toWIN ONLINE

Page 9: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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

Page 10: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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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Page 12: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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?

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We use a myriad of tools

Nakadi

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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 CaseNotebook/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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ProductionizingMLPipelinesAt 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.pysrc/lambdas/endpoint.pycf.yamlci-cd.yaml

CF:1. Step functions definition2. Trigger rule3. 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}

Page 27: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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Productionizing ML: Speed, with simplicity

{experiment_id_ts}-{build_number}

Page 28: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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Productionizing ML: Speed, with simplicity

Page 29: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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Productionizing ML: Speed, with cost efficiency

Page 30: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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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?

Page 32: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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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?

Page 33: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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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?

Page 34: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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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?

Page 35: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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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

Page 36: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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ML pipelines should be safe and understandable

How did the training progress?

Page 37: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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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?

Page 38: Machine Learning and Sagemaker at Zalando Mark… · Machine Learning and Sagemaker at Zalando Suhas Guruprasad ML Engineering Lead Zalando SE suhas.guruprasad@zalando.de. 2 Put images

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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 traffic routed to which model?

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ML pipelines should be safe and understandable

How is the model endpoint performing?

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Scaling ML at Zalando