MLOps Engineering: From Models to Production

A hands-on, project-based course that takes you from containerizing ML workloads to building fully automated, monitored, and cloud-deployed machine learning pipelines — including serving large language models efficiently on GPUs. You'll master Docker, Kubernetes, CI/CD, MLflow, infrastructure as code, production monitoring, and modern LLMOps (vLLM, quantization, KServe, GPU-aware scaling) while building a portfolio of deployable systems.

25 lessons · 6 modules

Module 1

Containerization & Foundations for ML Workloads

01 Setting Up Your MLOps Toolchain and Workflow
02 Containerizing a Machine Learning Model with Docker
03 Building a Model Inference API with FastAPI in Docker
04 Multi-Container Apps with Docker Compose

Module 2

Experiment Tracking, Pipelines & Model Deployment

01 Tracking Experiments and Models with MLflow
02 Managing Model Versions with the MLflow Model Registry
03 Building Reproducible Data Pipelines
04 Automating End-to-End Training and Deployment Workflows

Module 3

Orchestration, CI/CD & Infrastructure as Code

01 Deploying Models to Kubernetes
02 Scaling and Managing Model Services on Kubernetes
03 Building a CI/CD Pipeline for ML with GitHub Actions
04 Provisioning Infrastructure as Code with Terraform

Module 4

Cloud Deployment, Monitoring & Capstone

01 Deploying an ML Platform to the Cloud
02 Monitoring Model Performance and Detecting Drift
03 Implementing Alerting and Automated Retraining Triggers
04 Capstone: Build an End-to-End Production MLOps System

Module 5

LLMOps & GPU Serving

01 Serving LLMs Efficiently with vLLM
02 Optimizing LLM Inference: Quantization and Compiled Runtimes
03 Scaling LLM Serving on Kubernetes
04 LLMOps in Production: Observability, Evaluation, and Cost
05 Capstone: Build and Operate a Production LLM Serving System

Module 6

Capstone & Career Launch

01 Capstone Showcase: Package Your MLOps Platform as Portfolio Evidence
02 Building Your MLOps Portfolio
03 Resume and ATS for MLOps Engineers
04 LinkedIn and the MLOps Job Search