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notes and excercises for the mlops-zoomcamp from DataTalksClub

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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

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notes and excercises for the mlops-zoomcamp from DataTalksClub

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