GDD X Mollie

Transcript

1. From notebook to production in Vertex AI Wessel Huising & Daniel van der Ende
2. • Data & AI Consultancy and Training • We help organizations be successful with Data and AI • Mix of Data Engineers, Data Scientists, Machine Learning Engineers, Analytics Engineers & Analytics Translators • Based in Amsterdam & Eindhoven • Part of Xebia Data & AI • The best Payment Service Provider out there • Founded in 2004 by Adriaan Mol • Mission to simplify financial services by creating world-class products • Currently active for merchants in European Economic Area (EEA), Switzerland, and the United Kingdom
3. The challenge
4. • Low effort • Highly depending on one specific Data Scientist • Prone to human errors • Labor intensive Manual predictions are convenient Data Scientists ≠ Software Engineers • Data Scientist tend not to have traditional Software Engineering background • Tend to lack understanding of DevOps ML models are something else • Different than regular software artifacts • There is significant overlap Machine Learning models to production is hard
5. The classic ML routine
6. INPUT DATA The classic ML routine
7. INPUT DATA The classic ML routine Lack of a centralized data source
8. INPUT DATA The classic ML routine Code not living in a Python package
9. INPUT DATA The classic ML routine Missing lineage tracking of artifacts
10. INPUT DATA The classic ML routine No version control of the code
11. INPUT DATA The classic ML routine No automatic or scheduled predictions
12. INPUT DATA The classic ML routine Predictions all over the place
13. What is MLOps? and should you want it?
14. • Bringing a model into production state • Reliable & automated service but comes with extra costs • MLOps is a End-to-End process • Results in great ML software MLOps = Data + ML + DevOps
15. The big decision
17. The big decision Open Source solutions Managed solutions
18. What is Vertex AI
19. “Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.” What does Google tell us?
20. Vertex AI has a few (important) components The Buzzword Bingo And more… Metadata Endpoints Models Pipelines Workbench Features Datasets
21. Vertex AI has a few (important) components The Buzzword Bingo And more… Metadata Endpoints Models Pipelines Workbench Features Datasets
22. From notebook to production
24. Goal: Use machine learning to create a model that predicts which passengers survived the Titanic shipwreck
25. In a Workbench we can do “traditional” Data Science without any limitations or strings attached. Each DS has their own Virtual Machine Each VM: • Has access to data • Is persistent • Can be configured to work with VSCode or PyCharm on your local machine • Can have the specs you need/want Step 1: Let’s explore the problem! Workbench
26. Time to move our code out of notebooks! In this step, we’ll: • Use the Pipeline components that Google provides out of the box to setup a training pipeline. • Train our model and output Datasets and Models. • Deploy our model to an Endpoint so our model is available for consumption by downstream users. Step 2: Deploy it as if you’re Google Metadata Models Pipelines Datasets Endpoints
27. Let’s take a step back… Good Not so good ❌ Automated Train/Test splits are nice, but also opaque and not very configurable ❌ Where is the model evaluation step? ❌ Everything disappears into one big “train the model” step, including preprocessing. ✅ We have an ML Pipeline ✅ It’s all code ✅ It can be scheduled and kicked off automatically ✅ Everything now is traceable
28. To mitigate the downsides, while keeping the upsides, we use the Kubeflow API It integrates well with Vertex AI and can be easily customized Mollievert is our package with customized components to simplify and clarify our ML Pipelines Step 3: Now Mollie-fy it! Metadata Models Pipelines Datasets Endpoints
29. Wrapping up Mollie’s ML Platform enables robust MLOps practices by: • Making it easier to go to production without lowering the bar • Empowering Data Scientists and ML Engineers with tooling • Defining a ‘Golden Path’ to production, but allowing customization if desired.