hiimivantang/e2e-ml-workshop

🤖 e2e-ml-workshop 🤖

00-setup.py

• Downloading fraud model training datasets from Google drive.
• Original datasets are from https://www.kaggle.com/competitions/ieee-fraud-detection/data
• DLT with autoloader

01a-fraud detection model building.py

• Show high-level solution architecture
• Feature engineering
• Registering features into multiple feature tables
• Model training and experiment auto-tracking with mlflow
• Log model to register feature lookup logic

(julie) 02-feature store deep dive

• Adding new features to existing feature table
• Merge/upsert feature tables
• Publishing features to online store i.e. DynamoDB

03-MLOps CI/CD and webhooks

• Illustrate how CI/CD tooling (e.g. Jenkins) is integrated with model registry
• Setup webhooks for trigger CI build, i.e. model signature and bias testing

04-Realtime inference with automatic feature lookup (from online store)

• Illustrate how serverless model endpoint is integrated with offline/online feature store for automatic feature lookup
• Deploy model serving endpoint
• Score the deployed model via REST API request

Resources:

Feature store docs

• https://docs.databricks.com/machine-learning/feature-store/feature-tables.html#work-with-feature-tables

Online feature stores

• https://docs.databricks.com/machine-learning/feature-store/online-feature-stores.html

CI Build / model testing and evaluation

• https://docs.databricks.com/mlflow/model-registry-webhooks.html
• http://knowledge-repo-1712701941.us-east-2.elb.amazonaws.com/post/ML/05_ops_validation.kp

Data and model monitoring

• https://www.databricks.com/p/webinar/2021-10-20-hands-on-workshop-unified-ml-monitoring-on-databricks
• https://www.databricks.com/session_na21/drifting_away-testing-ml-models-in-production
• https://www.databricks.com/blog/2019/09/18/productionizing-machine-learning-from-deployment-to-drift-detection.html