Starting this MLOps Zoomcamp

It's provided by DataTalksClub

Module 1: Introduction

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

Module 2: Experiment-Tracking

• Experiment Tracking into
• Getting started with MLflow
• Experiment tracking with MLflow
• Model management
• Model registry
• Mlflow in practice
• Homework

Module 3: Orchestration

• Workflow orchestration
• Prefect 2.0
• Turning a notebook into a pipeline
• Deployment of Prefect Flow
• Homework

Module 4: Deployment

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

Module 5: Model Monitoring

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

Module 6: Best Practices

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

Project

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

More details

Module 7: Processes

• CRISP-DM, CRISP-ML
• ML Canvas
• Data Landscape canvas
• MLOps Stack Canvas
• Documentation practices in ML projects (Model Cards Toolkit)