Post on 26-May-2020
Kshitij Kumar,VP Data InfrastructureZalando SEKshitij.Kumar@zalando.de
Machine Learning and Sagemaker at Zalando
Suhas GuruprasadML Engineering LeadZalando SEsuhas.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 PLATFORMFOR FASHION
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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
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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& SCALABLEtechnology solutions
> 2,000employees at
international tech locations8
HQsin Berlin
help our brand toWIN 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 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}
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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 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
We’re hiring!
suhas.guruprasad@zalando.dekshitij.kumar@zalando.de