The content of this repsitory is the result of following the ml-ops-zoomcamp given by Data Talks Club

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 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

Module 3: Orchestration and ML Pipelines

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

Module 4: Model Deployment

• Batch vs online
• For online: web services vs streaming
• Serving models in Batch mode
• Web services
• Streaming (Kinesis/SQS + AWS Lambda)
• Homework

Module 5: Model Monitoring

• ML monitoring vs software monitoring
• Data quality monitoring
• Data drift / concept drift
• Batch vs real-time monitoring
• Tools: Evidently, Prometheus and Grafana
• Homework

Module 6: Best Practices

• Devops
• Virtual environments and Docker
• Python: logging, linting
• Testing: unit, integration, regression
• CI/CD (github actions)
• Infrastructure as code (terraform, cloudformation)
• Cookiecutter
• Makefiles
• Homework

Module 7: Processes

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

Project

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