Digital Innovation Partner

Effective MLOps


This training aims to teach practical aspects of productionizing ML services from collecting requirements to model deployment and monitoring.
Benefits: Master a set of tools and practices that helps you create a reliable Machine Learning model and make it to production
Target Audience: Data scientists and ML engineers. Also, software and data engineers interested in learning about putting ML in production.

The Program

Module 1 : Data, Model & Experiment Management

  • What is MLOps
  • MLOps maturity levels
  • Introduction and data understanding
  • Data cleaning and modeling
  • Data, code, experiment versioning

Key learnings

  • Learn ML & MLOps fundamentals
  • Build and compare an end-to-end ML models
  • Master Version Control System (DagsHub, GitHub)

Module 2 : Tooling, Infrastructure & Deployment

  • Backend and Frontend parts (Create restAPI using FastAPI & Create frontend using Streamlit framework)
  • Containerization (Docker)
  • Deploy the application to cloud provider (AWS, Heroku)
  • CI/CD/CT pipeline (Jenkins, GitHub Actions)

Key learnings

  • Master deploying ML system
  • Get hands-on experience in automatisation process for ML system

Module 3 : Serving, Testing and Validating the ML system

  • Testing: unit, integration
  • Testing data and models using Deepchecks
  • Monitoring ML-based services
  • Configure Arize AI as Machine Learning monitoring system

Key learnings

  • Get hands-on experience in testing and monitoring for ML system

Effective SRE

Effective MLOps

ML for Life Sciences

ML4 DevOps

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