Chelsea AI Ventures
Technical AI Training Developer Pathway 8 Weeks (Online, approx. 4-5 hours/week) or 3 Day Intensive (In-Person, London)

Modern Machine Learning Practices: The MLOps Lifecycle

Bridge the gap between a model in a Jupyter notebook and a scalable, reliable, automated production system

Target Audience

The ML/DevOps Engineer

Core Value

Bridge the gap between a model in a notebook and a scalable, reliable production system

Key Differentiator

A practical guide to the complete MLOps lifecycle, from CI/CD to model monitoring and retraining

Learning Objectives

  • Design and implement CI/CD pipelines for machine learning models
  • Use Docker and Kubernetes for containerizing and orchestrating ML services
  • Manage cloud infrastructure for ML workloads using Infrastructure as Code (Terraform)
  • Implement model registry and experiment tracking systems using MLflow
  • Design and deploy monitoring solutions for data drift and model performance degradation
  • Architect automated model retraining and redeployment pipelines

Prerequisites

Experience with machine learning, Python, and basic knowledge of cloud platforms.

Course Structure

Weeks 1-2: Introduction to MLOps & Containerization

MLOps principles, development vs production gaps. Docker fundamentals, containerizing ML applications.

Activities:

  • Containerize a trained model
  • Build multi-stage Docker pipelines

Weeks 3-4: CI/CD for Machine Learning & Experiment Tracking

Week 3: ML-specific CI/CD challenges, automated testing. Week 4: MLflow for experiment tracking and model registry.

Activities:

  • Build GitHub Actions ML pipeline
  • Implement comprehensive MLflow tracking

Weeks 5-6: Model Deployment & Infrastructure as Code

Week 5: Kubernetes for ML, scaling strategies. Week 6: Terraform for ML infrastructure, cloud cost optimization.

Activities:

  • Deploy model to Kubernetes
  • Write Terraform modules for ML infrastructure

Weeks 7-8: Model Monitoring & Automated Retraining

Data drift detection, performance monitoring, alert systems. Automated retraining pipelines, A/B testing.

Activities:

  • Build drift detection system
  • Implement automated retraining pipeline

Topics Covered

MLOps principles and best practices
Docker containerization for ML
CI/CD pipelines for ML projects
Automated testing for ML code
MLflow for experiment tracking
Model registry patterns
Kubernetes orchestration
Infrastructure as Code (Terraform)
Cloud cost optimization
Data drift detection
Model performance monitoring
Automated retraining strategies

Capstone Project

Build complete MLOps pipeline: automated testing, containerization, deployment to staging, model registry entry, and monitoring dashboard.

Why This Course Matters

The hardest part of machine learning isn’t building models—it’s deploying them reliably at scale. Studies show that 87% of ML projects never make it to production. Of those that do, most fail within the first year due to data drift, infrastructure issues, or maintenance nightmares.

MLOps is the discipline that bridges this gap. It’s what separates hobby projects from systems that power real businesses. And it’s the skill set that distinguishes senior ML engineers from junior data scientists.

What Makes This Course Different

We don’t just teach tools—we teach the complete production lifecycle. You’ll learn not just how to deploy a model, but how to build systems that automatically detect when that model degrades, retrain on new data, and redeploy without human intervention.

Every module is grounded in real-world scenarios. You’ll tackle challenges like: What happens when your training data distribution shifts? How do you roll back a bad model deployment? How do you prove your model’s ROI to stakeholders? These are the questions that matter in production.

Course Philosophy

We believe MLOps is about building antifragile systems—systems that get stronger under stress rather than breaking. This means embracing automation, monitoring everything, and designing for failure from day one.

Throughout this course, we emphasize pragmatic solutions over perfect ones. The goal isn’t to build the most sophisticated pipeline, but the most reliable one. Simple systems that work beat complex systems that break.

Who Should Take This Course

This course is essential if you:

  • Have models stuck in notebooks that never reach production
  • Want to transition from data scientist to ML engineer
  • Are a DevOps engineer moving into ML infrastructure
  • Need to build reliable, scalable ML systems
  • Want to automate the tedious parts of ML deployment
  • Are responsible for ML systems that can’t afford downtime

If you’re ready to make machine learning work in the real world, this course provides the engineering foundation you need.

Ready to transform your team?

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