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Courses > MLOps – Machine Learning Operations for Developers & Data Teams

MLOps – Machine Learning Operations for Developers & Data Teams

Students
Last Updated: Aug 26, 2025

About Course

This 2-week intensive MLOps program is designed for developers, data scientists, and ML engineers who want to move machine learning models from experimentation to scalable, reliable production systems. The course takes a hands-on, project-driven approach using modern tools and workflows such as MLflow, DVC, Docker, Kubernetes, CI/CD pipelines, and cloud-native platforms like AWS Sagemaker and HuggingFace Spaces.

What Will You Learn?

  • Bridge the gap between ML model development and production deployment
  • Learn to build robust ML pipelines and automate deployments
  • Gain hands-on experience with CI/CD, Docker, and Kubernetes for ML
  • Monitor live models and detect data/model drift in real-time
  • Learn industry-standard practices in security, governance, and compliance
  • Build a full MLOps project with infrastructure diagram and deployment

Course Content

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Material Includes

  • GitHub project templates for ML pipelines and CI/CD
  • Sample notebooks for experiment tracking using MLflow/W&B
  • Docker + FastAPI deployment template
  • Monitoring dashboard setup guide (Grafana/Prometheus)
  • Capstone project guide and architecture diagram
  • Access to recorded sessions and slides

Requirements

  • Basic knowledge of Python and machine learning workflows
  • Familiarity with Git and command-line tools
  • Laptop with Linux/Ubuntu or WSL (Windows Subsystem for Linux)
  • Python 3.8+ and Docker installed
  • Google/GitHub account access for tools and repositories
  • Internet connection for live sessions and cloud platform access

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Audience

  • Machine Learning Engineers
  • Data Scientists working with production models
  • Backend Developers transitioning into ML/AI roles
  • DevOps Engineers exploring ML workflows
  • Final-year students with strong Python and ML background

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