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data Advanced 21 lessons

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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Secure Enrollment via SSL

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

MLOps Engineering FAQs

Kubernetes?

Concepts apply, but we focus on Docker/Cloud first.

Is this DevOps?

Yes, specialized DevOps for the unique needs of ML.

Cloud specific?

Tools taught are open source and cloud-agnostic.

Why MLflow?

It is the industry standard open-source MLOps tool.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

MLflow
Docker
FastAPI
GitHub Actions
Prometheus

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