mlops-zoomcamp2024

This is a repository to keep track of my progress during the MLOps Zoomcamp course.

Syllabus

Week 1(01-intro): Introduction

• What is MLOps
• MLOps maturity model
• Running example: NY Taxi trips dataset
• Why do we need MLOps
• Course overview
• Environment preparation
• Homework
• More details

Week 2(02-experiment-tracking): Experiment tracking and model management

• Experiment tracking intro
• Getting started with MLflow
• Experiment tracking with MLflow
• Saving and loading models with MLflow
• Model registry
• MLflow in practice
• Homework
• More details

Week 3(03-orchestration): Orchestration and ML Pipelines

• Workflow orchestration
• Mage
• More details

Week 4(04-deployment): Model Deployment

• Three ways of model deployment: Online (web and streaming) and offline (batch)
  • Web service: model deployment with Flask
  • Streaming: consuming events with AWS Kinesis and Lambda
  • Batch: scoring data offline
• Homework
• More details

Week 5(05-monitoring): Model Monitoring

• Monitoring ML-based services
  • Monitoring web services with Prometheus, Evidently, and Grafana
  • Monitoring batch jobs with Prefect, MongoDB, and Evidently
• More details

Week 6(06-best-practices): Best Practices

• Testing: unit, integration
• Python: linting and formatting
• Pre-commit hooks and makefiles
• CI/CD (GitHub Actions)
• Infrastructure as code (Terraform)
• Homework
• More details

Project

• End-to-end project with all the things above

Instructors

• Cristian Martinez
• Tommy Dang
• Alexey Grigorev
• Emeli Dral
• Sejal Vaidya