MLOps for Beginners: Deploy ML Models Like a Pro (2026)
Master Docker, GitHub Actions, MLflow, FastAPI & model monitoring in 4 weeks — go from training models locally to deploying them in production
Last updated: April 2026 • 9,800+ students enrolled
Key Takeaways — What you will master in 4 weeks:
- Containerize any ML model using Docker — write Dockerfiles, build images, run containers
- Set up a complete CI/CD pipeline with GitHub Actions — automated testing on every commit
- Track ML experiments, compare runs, and register models with MLflow
- Build a production-ready REST API with FastAPI to serve model predictions
- Monitor deployed models for data drift, latency spikes, and accuracy degradation
- Complete the full MLOps lifecycle: Train → Track → Containerize → Deploy → Monitor
- Build 3 portfolio projects covering the complete ML deployment pipeline
What You’ll Learn
Docker & Containerization
GitHub Actions CI/CD
MLflow Experiment Tracking
FastAPI Model Serving
Model Monitoring & Drift
Model Registry & Versioning
Automated ML Testing
Cloud-Ready Deployment
The Complete MLOps Pipeline You’ll Build
▶ End-to-End MLOps Flow
📊 Train Model
→
📈 MLflow Track
→
🐨 Docker Package
→
⚙ GitHub CI/CD
→
⚡ FastAPI Serve
→
🔍 Monitor
→
📈 MLflow Track
→
🐨 Docker Package
→
⚙ GitHub CI/CD
→
⚡ FastAPI Serve
→
🔍 Monitor
Full Curriculum — 4 Weeks, 20 Lessons
Week 1 — ML Lifecycle & Docker FundamentalsWeek 1
Lesson 1: The ML lifecycle problem — why 85% of ML projects never reach production
Lesson 2: Docker core concepts — images, containers, volumes, networking
Lesson 3: Writing your first Dockerfile for a Python ML app
Lesson 4: Docker Compose for multi-service ML apps
Project 1: Containerize a scikit-learn classification model
Week 2 — CI/CD with GitHub ActionsWeek 2
Lesson 5: GitHub Actions fundamentals — workflows, jobs, steps, runners
Lesson 6: Automated ML tests — unit tests for preprocessing, model validation
Lesson 7: Build and push Docker images on merge to main
Lesson 8: Environment management — secrets, env vars in CI/CD pipelines
Lesson 9: Continuous training — retrain model automatically on new data push
Week 3 — MLflow Experiment Tracking & Model RegistryWeek 3
Lesson 10: MLflow tracking — log parameters, metrics, artifacts per run
Lesson 11: Comparing experiments — find your best model visually
Lesson 12: Model registry — versioning, staging, production promotion
Lesson 13: MLflow + GitHub Actions — auto-register best model on CI pass
Project 2: Full experiment tracking pipeline for a regression model
Week 4 — FastAPI Deployment & Model MonitoringWeek 4
Lesson 14: FastAPI for ML — build a /predict REST endpoint in 30 minutes
Lesson 15: Input validation with Pydantic — prevent bad prediction requests
Lesson 16: Model monitoring basics — data drift, concept drift, prediction monitoring
Lesson 17: Alerting on model degradation — when to retrain
Project 3: End-to-end MLOps — train, register, deploy, and monitor a complete ML system
Prerequisites
- Python programming — comfortable with functions, classes, and pip install
- Basic ML knowledge — you should understand what train/test split means
- Git/GitHub basics — you should be able to commit and push code
- A computer with at least 8GB RAM (Docker requires some resources)
You do NOT need: DevOps experience, cloud accounts, deep learning knowledge, or Linux expertise.
Career Outcomes & Salary Benchmarks
MLOps Engineer
₹10–20 LPA (India)
Own the full ML deployment pipeline — from experiment tracking to production serving and monitoring
ML Infrastructure Engineer
₹15–30 LPA
Build and maintain the platforms and tools that data scientists use to train and deploy models at scale
DevOps + AI Engineer
₹12–24 LPA
Hybrid role combining DevOps skills with ML deployment — in huge demand at IT services companies
AI/ML Platform Engineer
₹18–40 LPA
Build internal ML platforms and developer tools at product companies and tech unicorns
Your Instructor
🛠
EngineeringHulk AI Team
Our MLOps curriculum is built by ML engineers who have deployed and maintained models in production at scale. The team has experience building ML platforms serving millions of predictions daily, and has trained 50,000+ students on our engineering education platform. Content is reviewed every quarter to stay current with the latest MLOps tools and practices.
What Students Say
★★★★★
“I was a data scientist who had never deployed anything. After Week 2 I had a real Docker container running my model. After Week 4 I had a full CI/CD pipeline. This course is transformative.”
Vikram Singh
Data Scientist → MLOps Engineer, Flipkart
★★★★★
“The MLflow section alone is worth the entire course. I used to track experiments in Excel spreadsheets. Now I have a proper experiment dashboard and my manager is very impressed.”
Deepa Krishnan
ML Engineer, Wipro AI Lab • Chennai
★★★★☆
“Clear, practical, and directly applicable. I completed Project 3 and used it as my portfolio project in 3 interviews. Got an offer from a product startup in Bangalore.”
Arjun Nair
B.Tech CSE 2025, NIT Calicut
Frequently Asked Questions
What is MLOps and why is it important in 2026?
MLOps (Machine Learning Operations) is the practice of deploying, monitoring, and maintaining ML models in production — reliably and at scale. According to Gartner, 85% of ML projects fail to reach production. MLOps is the discipline that solves this — bridging the gap between data scientists who build models and the production systems that serve predictions to millions of users.
How to deploy a machine learning model step by step?
Deploying an ML model involves 5 steps: (1) Serialize your trained model; (2) Build a FastAPI REST endpoint exposing a /predict route; (3) Containerize with Docker; (4) Automate testing and deployment with GitHub Actions CI/CD; (5) Monitor the deployed model for drift and performance degradation. This course covers all 5 steps with hands-on projects.
What MLOps tools should a beginner learn in 2026?
The essential MLOps toolstack for 2026: Docker (containerization), GitHub Actions (CI/CD), MLflow (experiment tracking + model registry), FastAPI (model serving), and monitoring tools. This course covers all of these with real projects.
What is the salary of an MLOps engineer in India in 2026?
Entry-level MLOps engineers earn ₹10–18 LPA. Mid-level (3–5 years) earn ₹20–40 LPA. Senior MLOps engineers / ML Infrastructure leads earn ₹40–70 LPA at top product companies. The combination of software engineering and ML makes MLOps a premium skill set.
Start Deploying ML Models Like a Pro
Join 9,800+ engineers who are already mastering MLOps with EngineeringHulk. Free course — just enrol and start building.
🎓 Certificate of Completion included