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Mastering MLOps: Complete Course on ML Operations

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Note: Labs for almost all modules can be performed on open source. So Koenig DC can be provided. Student can also install software’s on their own system.
Module 1: MLOps Fundamentals

  • Introduction to MLOps and its significance.
  • Challenges in traditional ML model management.
  • Solutions offered by MLOps.


Module 2: MLOps Toolbox

  • Applying MLOps tools for end-to-end projects.
  • Integration of tools: DVC, Git, MLFlow, and DagsHub.

Module 3: Model Versioning with MLFlow

  • Versioning and registering ML models with MLFlow.
  • MLlow’s role in managing ML lifecycle.

Module 4: Data Versioning with DVC

  • Capturing data and model versions with DVC.
  • On-premises and cloud storage integration.


Module 5: Creating Shared ML Repository

  • Utilizing DagsHub, DVC, Git, and MLFlow for versioning.
  • Collaborative ML model management.


Module 6: Auto-ML and Low-Code MLOps

  • Automation of ML model development with Auto-ML and Pycaret.
  • Streamlining model versioning, training, evaluation, and deployment.


Module 7: Explain ability and Auditability

  • Understanding model interpretability and explain ability.
  • Monitoring model performance and data drift with SHAP and Evidently.


Module 8: Containerized ML Workflow with Docker

  • Packaging code and dependencies using Docker.
  • Efficient distribution of Machine Learning applications.


Module 9: Deploying ML via APIs

  • Model deployment through API development with FastAPI and Flask.
  • Deploying APIs on Azure Cloud using containers.

Module 10: Deploying ML in Web Applications

  • Developing web apps with embedded ML models using Gradio and Flask.
  • Deploying to production in Azure via Docker containers.

Module 11: Automated ML Services with BentoML

  • Introduction to BentoML and its role in automated ML service development.
  • Putting BentoML services into production using Docker.
  • Integration of BentoML with MLFlow.

Module 12: CI/CD with GitHub Actions and CML

  • Introduction to GitHub Actions and Continuous Machine Learning (CML).
  • Practical lab: GitHub Actions for MLOps CI/CD.

Module 13: Model Monitoring with Evidently A

  • Monitoring models and services using Evidently AI.
  • Identifying data drift and evaluating model quality.

Module 14: Model Monitoring with Deep checks

  • Components of Deep checks: checks, conditions, and suites.
  • Hands-on experience with Data Integrity Suite, Train Test Validation Suite, Model.
  • Evaluation Suite, and Custom Performance Suite.

Module 15: Complete MLOps Project

  • Developing an ML model from scratch.
  • Validating code and pre-processing data.
  • Versioning with MLFlow and DVC.
  • Sharing repository with DagsHub and MLFlow.
  • Building an API with BentoML.
  • Creating a Streamlet app.
  • Implementing CI/CD with GitHub Actions.
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Mastering MLOps: Complete Course on ML Operations

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