H&M uses machine learning for various use cases including logistics, production, sales, marketing, and design/buying. MLOps principles like model versioning, reproducibility, scalability, and automated training are applied to manage the machine learning lifecycle. The technical stack includes Kubernetes, Docker, Azure Databricks for interactive development, Airflow for automated training, and Seldon for model serving. The goal is to apply MLOps at scale for various prediction scenarios through a continuous integration/continuous delivery pipeline.

1. 1
General Information
APPLY MLOPS AT SCALE
Keven Wang @ H&M

2. 2
General Information
H&M AI use cases
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Analytics and Data Platform
Logistics
Production Sales Marketing
Design / Buying
Assortment quantification
Fashion Forecast
Allocation Markdown Online
Markdown Store
Personalized Promotions,
Recommendations & Journeys
Movebox
Knowledge &
Best Practice
AI exploration
and Research
Rapid Dev
enablement
AI platform

3. General Information
Version compatibility
Reproducibility
Model approval process
Model format
Experiment strategy
Fast feedback loop
Model traceability
Model evaluation
Automated model training
MLOps
Scalability

4. 4
General Information
Machine Learning Process
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Model Deployment
Model training
Data acquisition
Data
preparation
Feature
Engineering
Model training
Model
repository
Unseen data
acquisition
Data
preparation
Transform data
into feature
Model
prediction Results
Deployment orchestration
Data
storage
Training orchestration
Model and data versioning
Automated, e2e feedback loop
e2e monitoring

5. 5
General Information
MLOps tech stack
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6. 6
General Information
Interactive model training
F E B 2 0 2 1
Kubernetes
Container
Registry
Triggering
CI Orchestrator
Model
repository
Azure Databricks
1 Code commit
2 code static check,
unit test,
Packaging
3.2 Trigger pipeline
4.3 Commit model
5.1 Fetch model
5.2 Build container image
6 Push image
7 Auto deploy
PyCharm
3.1 Push
to DBFS
4.2 log model info
4.1 job execution

7. Demo 1
Interactive Model Development

8. 8
General Information
Automated model training 1
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Scenario 1
• Geo location l1
• Product type p1
• Time t1
Scenario 2
• Geo location l2
• Product type p2
• Time t2
Scenario 3
• Geo location l3
• Product type p3
• Time t3
Scenario i
• Geo location li
• Product type pi
• Time ti
Scenario set
Source
data
Prep
data
Feature
engine…
Train Optimize
Source
data
Prep
data
Feature
engine…
Train Optimize
Source
data
Prep
data
Feature
engine…
Train Optimize
Source
data
Prep
data
Feature
engine…
Train Optimize
Databricks Cluster
Databricks Cluster
Databricks Cluster
VM
VM
Container

9. 9
General Information
Automated model training 2
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Scenario
set
Scenario
task 1
Source
data
Prep data
Feature
engine…
Train Optimize
Scenario
task 1
Source
data
Prep data
Feature
engine…
Train Optimize
Scenario
task 1
Source
data
Prep data
Feature
engine…
Train Optimize
Scenario
task 1
Source
data
Prep data
Feature
engine…
Train Optimize
Scenario
set
Scenario
task 1
Source
data
Prep data
Feature
engine…
Train Optimize
Scenario
task 1
Source
data
Prep data
Feature
engine…
Train Optimize
Scenario
task 1
Source
data
Prep data
Feature
engine…
Train Optimize
Scenario
task 1
Source
data
Prep data
Feature
engine…
Train Optimize
DAG
Scenario
set
Scenario 1
Source
data
Prep data
Feature
engine…
Train Optimize
Scenario 2 Source
data
Prep data
Feature
engine…
Train Optimize
Scenario 3
Source
data
Prep data
Feature
engine…
Train Optimize
Scenario i
Source
data
Prep data
Feature
engine…
Train Optimize
Databricks
Cluster
Databricks
Cluster
Databricks
Cluster
Azure Kubernetes Service
Container Registry
Airflow
Logs
Airflow
dags
Persistent
Volume
Airflow
Webserver
Airflow
Scheduler
Kubernetes Pod
Azure File share

10. 10
General Information
Model management and lifecycle
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Staging Production
Model Aproval
Back Test
Model Development
PR pipeline
Back test
pipeline
Trainning CI
pipeline
CD – Staging
Pipeline
CD – prod
pipeline CI/CD pipeline
develop feature
Pull Req
Infra as code
#dev #stage #prod
Infra as code Infra as code

11. Demo 2
Model Serving

12. 12
General Information
Online model serving - Seldon
F E B 2 0 2 1

13. 13
Take away
Leverage cloud native service
Problem, Process and Architecture
DS & SW engineering

14. General Information
THANK YOU