Month 6 — MLOps and Production
🎯 Goal of this month
**This month is the most important for your main goal.**To become an ML Engineer / MLOps Engineer, the knowledge from this month becomes the center of your portfolio.
By the end of the month you will be able to:
- Know the MLOps lifecycle from start to finish
- Track experiments and version models with MLflow
- Ensure data versioning and reproducibility with DVC
- Serve ML models via FastAPI + BentoML/TorchServe
- ML deployment on Docker + Kubernetes
- Monitoring and drift detection with Prometheus + Grafana + Evidently AI
- Orchestrate ML pipelines with Apache Airflow
- ML CI/CD with GitHub Actions
Weekly breakdown
| Week | Topic | Time |
|---|---|---|
| Week 1 | MLOps intro + MLflow + DVC | 10-12 hours |
| Week 2 | FastAPI serving + Docker + K8s | 12-15 hours |
| Week 3 | Monitoring + CI/CD | 10-12 hours |
| Week 4 | Airflow + End-to-End capstone | 12-15 hours |
Chapter order
- Introduction to MLOps
- MLflow — Experiment tracking
- DVC — Data Versioning
- FastAPI + ML Serving
- Docker and Kubernetes
- Model Monitoring
- CI/CD for ML
- Airflow and Prefect
- Exercises
What can you do by the end of the month?
- Build a full production ML system: training → versioning → serving → monitoring
- ML model deployment on Kubernetes
- Automatically detect model degradation via drift detection
- CI/CD pipeline for ML (test, validate, deploy)
- Weekly retraining via Airflow DAG
- Meet the MLOps Engineer requirements written in job descriptions
Tip for Backend Dev — this month is your golden month!
It’s in this month that your existing knowledge gives you a strong advantage:
| Backend knowledge | Application in MLOps |
|---|---|
| Docker, docker-compose | ML containers |
| PostgreSQL | Feature store, prediction logs |
| Redis | Model cache, feature cache |
| Celery, Kafka | Async inference, streaming |
| GitHub Actions / GitLab CI | ML CI/CD |
| Nginx, load balancing | ML model serving |
| Prometheus, Grafana | ML monitoring |
| REST API design | ML inference endpoints |
| Async/await | Concurrent inference |
| Microservices | ML services architecture |
Most ML Engineers (coming from data scientist roles) learn these things from zero. Your starting position is much higher than theirs.
Cloud Cost (optional)
This month you’ll need cloud services. Options:
- AWS Free Tier($300 credit for new accounts)
- GCP Free Tier($300 credit)
- DigitalOcean($200 credit student/coupon)
- Hetzner — cheapest (€5/month server)
- Local Kubernetes(minikube, kind, k3s) — free, enough for small projects
**Advice:**main exercises on local Docker + minikube, use real cloud only for capstone.
Start
Start with Introduction to MLOps.