MLOPS: MACHINE LEARNING OPERATIONS

About Course

Building a machine learning model is only half the job. This course covers everything it takes to get that model into production and keep it running reliably: version control for code and data, experiment tracking, containerization, CI/CD, cloud deployment, and the monitoring and retraining loop that keeps a model useful after launch.

Every lesson is grounded in the tools real ML teams actually use, MLflow, Docker, FastAPI, GitHub Actions, and GCP, so you finish with a practical, end-to-end MLOps workflow you can apply to your own projects, not just a list of buzzwords.

Structure a machine learning project for production, not just a notebook
Version control both code and data using Git, GitHub, and DVC
Track experiments and orchestrate ML pipelines with MLflow and ZenML
Package and containerize models for reliable, reproducible deployment with Docker
Build and serve models through real APIs with FastAPI, and interactive demos with Streamlit
Set up CI/CD pipelines for machine learning systems
Deploy machine learning workloads to the cloud on GCP
Monitor deployed models and build continuous training pipelines to handle data drift
Safely roll out new model versions using A/B testing and canary deployments

Course Content

MLOps Foundations

Introduction to MLOps and the Model Deployment Lifecycle
From Notebook to Production: Structuring ML Projects for Deployment
MLOps Foundations Quiz

Version Control

Experiment Tracking and Pipeline Orchestration

Packaging and Containerization

Serving Machine Learning Models

Continuous Integration and Deployment

Monitoring and Continuous Improvement

Capstone

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MLOPS: MACHINE LEARNING OPERATIONS

About Course

What Will You Learn?

Course Content

MLOps Foundations

Introduction to MLOps and the Model Deployment Lifecycle

From Notebook to Production: Structuring ML Projects for Deployment

MLOps Foundations Quiz

Version Control

Version Control 1: Git and GitHub for ML Projects

Version Control 2: Data Version Control (DVC)

Version Control Quiz

Experiment Tracking and Pipeline Orchestration

Experiment Tracking with MLflow and Weights & Biases

Pipeline Orchestration with ZenML and Other Workflow Tools

Experiment Tracking and Pipeline Orchestration Quiz

Packaging and Containerization

Model Serialization and Packaging for Deployment

Containerizing ML Applications with Docker

Packaging and Containerization Quiz

Serving Machine Learning Models

Building ML APIs with FastAPI

Interactive ML Applications with Streamlit

Scalable Model Serving and Deployment Architecture

Serving Machine Learning Models Quiz

Continuous Integration and Deployment

CI/CD Pipelines for Machine Learning Systems

Cloud Deployment Basics for Machine Learning on GCP

Continuous Integration and Deployment Quiz

Monitoring and Continuous Improvement

Model Monitoring, Logging, and Observability with Pydantic Logfire

Data Drift and Model Retraining: Building Continuous Training Pipelines

A/B Testing and Canary Deployments for ML Models in Production

Monitoring and Continuous Improvement Quiz

Capstone

End-to-End MLOps Project: From Model to Production

Capstone Quiz

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