MLOps Engineering

Take models from notebook to production. Master model versioning, serving, monitoring pipelines, and CI/CD for Machine Learning.

Building a model is easy; keeping it running in production is hard. MLOps (Machine Learning Operations) bridges the gap between Data Science and DevOps. This course teaches you to package models with Docker, track experiments with MLflow, and deploy APIs using FastAPI. You will learn to monitor models for 'data drift' where performance degrades over time, and build automated CI/CD pipelines to retrain and redeploy models automatically. Essential for any engineer working in a mature AI team.

✅ 100% Free & Lifetime Access

⏱️ 5-Minute Lessons (Bite-sized learning)

🚀 21-Lesson Path (Independent modules)

📱 Mobile Friendly (Learn anywhere)

MLOps Pros

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Complete Course Syllabus

1

Model Lifecycle

From experimentation to registration and staging.
2

Experiment Tracking

Using MLflow to log params, metrics, and artifacts.
3

Model Serving

Wrapping models in FastAPI and Docker containers.
4

Pipeline Automation

Triggering retraining workflows with GitHub Actions.
5

Monitoring & Drift

Detecting when production data diverges from training.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary

$130k - $180k

Career Paths

MLOps Engineer $130k-$180k

AI Platform Engineer $140k-$190k

Machine Learning Eng $125k-$170k

What You Will Learn

Deploy machine learning models as scalable REST APIs

Track experiments and model versions using MLflow

Detect data drift and model degradation in production

Automate model retraining pipelines using CI/CD tools

Containerize ML applications for consistent deployment

Skills You Will Gain

MLflow Docker Model Serving Data Drift Detection CI/CD for ML

Who Is This For

Data Scientists

DevOps Engineers

Software Engineers

Prerequisites

Machine Learning

Basic DevOps