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Apply MLOps at Scale
The document outlines H&M's AI journey and the implementation of MLOps at scale, detailing the evolution from initial proof of concepts in 2016 to establishing a robust AI function by 2019. It describes the key architectural elements of their machine learning processes and the collaborative efforts across various teams to optimize AI-driven capabilities in retail. Additionally, it emphasizes the importance of model scalability, reproducibility, and ongoing feedback to refine their AI initiatives and ensure they contribute to business value.
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Apply MLOps at Scale
- 1. Apply MLOps AtScale Keven(Qi) Wang Linkedin: https://www.linkedin.com/in/kevenqiwang/ Medium: https://medium.com/@kevenwang_33862 Lead AI Architect @ H&M
- 2. Agenda AI journey @H&M Quickfacts and use cases Reference Architecture gen1 ML process and ML training Reference Architecture gen2 MLOps and Operationalize AI
- 3. AI journey @H&M Quickfacts and use cases
- 4. General Information 74 markets 5000+stores 177,000 employees More than Over Sales including VAT SEK 210 billion (2018) E-commerce in 51 markets
- 5. Our Journey 2016 Exploration Run initialPoCs Test AA appetite & applicability 2017 Initiation Industrialize early use cases Defining organization and capability needs Establishing the IT / data environment 2018 Establish AA & AI function Roll-out & hand over of successful pilots Establishing AA-WoW, team, governance 2019 AA Leader Increasingly data & algo-driven retail business Analytical support across entire value chain Strong internal AA teams Engage in partnership with strong AI players 2022 AI Leader of the Fashion Industry Lead the frontier of AI at scale in delivering customer value Global leader in developing talent pools and supporting AI hubs and networks AI-powered tools and capabilities supporting core processes and business decisions in all functions World leading ecosystem of cutting edge AI partners Today Algo library, IT platform, Business Impact
- 6. H&M use cases Analyticsand Data Platform LogisticsProduction Sales MarketingDesign / 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
- 7. AI @ H&Mquick facts 100+ co-located FTEs Growing # of colleagues 30+ different nationalities Several nationalities Combined teams Sprints Standups Product mgmt. Epics Algo Cloud New ways of working Consultants HAAL Azure Databricks
- 8. Reference Architecture gen1 MLprocess and ML training
- 9. Starting point –fragemented architecture
- 10. ML Process andTooling 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 Datastorage Training orchestration Data Lake Store Model and data versioning Automated, e2e feedback loop e2e monitoring
- 11. Interactive model development Kubernetes Container Registry Triggering CIOrchestrator 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
- 12. Automated model trainingpipeline 1 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
- 13. Automated model trainingpipeline 2 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 RegistryAirflow Logs Airflow dags Persistent Volume Airflow Webserver Airflow Scheduler Kubernetes Pod Azure File share Airflow MetaDB
- 14. Trick for Airflowdependency challenge Actual python method Little trick: python_callable Call the function without import the module For more detail, check this blog post: https://medium.com/@kevenwang_33862/machine-learning-in-production-2-large-scale-ml-training-889cde94f26d
- 15. 15 General Information Evolve toscale and industrialize across H&M Make AI available for product teams across H&M Group Facilitate scalability and specialization Continue to build word-class AI products, engines and core components Proven the value in use case by use case Now: to reach next level we need to industrialize and scale AI across H&M
- 16. Reference Architecture gen2 MLOpsand Operationalize AI
- 17. General Information Version compatibility Reproducibility Approveprocess Model format Experiment strategy Feedback loop Model traceability Model metadata Deployment strategy MLOps Scalability
- 18.
- 19. Model development -Interactive VS Automated ▪ AI product lifecycle ▪ Notebook and Python modules ▪ Container as first class citizen ▪ Airflow VS Kubeflow
- 20. Model serving –deployment strategy Router Model 1.1 Router (canary) Model 1.1 Model 1.2 Router (shadow) Model 1.1 Model 1.2 Router Model A1 Model A2 Model A3 Router Model A1 Model A2 Model A3 Reward System Release Strategies Experiment Strategy A/B test Experiment Strategy Multi-armed Bandit
- 21. Model serving –Inference Graph Router 1 (Multi-armed Bandit) Router 2 (A/B test) Model B1 Model B2 Model A1 Model A2 Model A3 Input Transformer Output Transformer
- 22. Model management andlifecycle Staging ProductionModel AprovalBack TestModel 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
- 23. Take away ▪ Problem,Process and Architecture ▪ Platform approach ▪ Leverage cloud native service
- 24. Feedback Your feedback isimportant to us. Don’t forget to rate and review the sessions.