MLOps Course - Deploy ML Models to Production 2026

MLOps Course - Deploy ML Models to Production 2026

Here's the dirty secret: Most ML models never make it to production.

Companies build amazing models in Jupyter notebooks, then hit a wall. How do you put it in production? How do you update it with new data? How do you monitor if it's breaking? How do you serve 1 million requests per day?

That's MLOps. And it's why MLOps engineers earn 15-25 LPA—they're the bridge between data scientists and production systems.

This guide explains why MLOps matters, what it involves, and how our 8-week MLOps course takes you from model-in-notebook to production-ready systems.

The ML Lifecycle is Bigger Than Model Training

Most ML courses stop after model training. That's like building a car and stopping before assembly.

The full lifecycle:

1. Development: Data collection, EDA, model training (that's what most courses cover)
2. Production: Deployment, monitoring, retraining (that's what we focus on)
3. Operations: Scaling, cost optimization, incident response (that's what companies need)

Companies don't hire for steps 1-2 alone. They hire for the whole pipeline. That's MLOps.

What You'll Actually Learn

Week 1: ML Lifecycle & Production Challenges

Why is production different from development?

What you'll cover:

• Data drift and concept drift
• Model monitoring and alerting
• A/B testing and canary deployments
• Cost and latency constraints
• Real examples of production failures

Real understanding: You'll see why production is hard before learning how to solve it.

Week 2: Model Packaging & Containerization

Taking a notebook model and containerizing it.

What you'll cover:

• Model serialization (pickle, ONNX, SavedModel)
• Docker containerization
• Building efficient Docker images
• Model versioning
• Dependency management

Real projects:

• Package an ML model as a Docker container
• Create a model registry
• Version control for models and data

Week 3: Serving & API Development

Exposing your model as a service.

What you'll cover:

• REST APIs for models (Flask, FastAPI)
• gRPC for high-performance serving
• Model serving platforms (TensorFlow Serving, Seldon, KServe)
• Handling batch and real-time inference
• API testing and validation

Real projects:

• Build a REST API for a model
• Benchmark different serving frameworks
• Create a prediction service with error handling

Week 4: CI/CD & Automation

Automating the deployment pipeline.

What you'll cover:

• Continuous Integration for ML (GitHub Actions, GitLab CI)
• Continuous Deployment pipelines
• Model validation and testing
• Automated model evaluation
• Infrastructure as Code (Terraform, CloudFormation)

Real projects:

• Build a CI/CD pipeline for an ML model
• Automate model retraining
• Set up automated testing and validation

Week 5: Monitoring & Observability

Keeping tabs on your models in production.

What you'll cover:

• Performance monitoring (accuracy, latency, throughput)
• Data drift detection
• Model monitoring tools (Evidently, WhyLabs, Arize)
• Alerting and incident response
• Logging and observability

Real projects:

• Set up monitoring for a production model
• Detect data drift in real data
• Create alert rules for model degradation

Week 6: Scaling & Infrastructure

Running models at scale.

What you'll cover:

• Kubernetes for orchestration
• Distributed inference
• GPU and TPU optimization
• Cost optimization strategies
• Multi-cloud deployment

Real projects:

• Deploy a model on Kubernetes
• Optimize inference latency
• Build a distributed pipeline

Week 7: Data & Feature Management

Managing the data side of ML systems.

What you'll cover:

• Feature stores and feature management
• Data versioning
• Data quality monitoring
• ETL/ELT pipelines
• Real-time feature generation

Real projects:

• Build a feature pipeline
• Implement a feature store
• Create data validation rules

Week 8: Capstone Project

Build an end-to-end production ML system.

Choose your capstone:

• Recommendation system: Model serving + feature pipeline + monitoring
• Fraud detection: Real-time API + monitoring + retraining
• Computer vision pipeline: Model serving + batch processing + monitoring
• NLP application: API + streaming data + model updates

MLOps Isn't Optional

Some companies skip it: "We'll just run it on our server."

Those are the same companies with:

• Models that break silently
• No way to update without downtime
• No idea why predictions changed
• Spending $10K/month on compute they don't need

MLOps prevents all of this.

Expected Outcomes

Technical Skills

• Package and containerize ML models
• Build production ML pipelines
• Deploy and serve models at scale
• Monitor models in production
• Automate model retraining and deployment
• Set up CI/CD for ML workflows

Career Outcomes

• Job titles: MLOps Engineer, ML Engineer (Ops), Platform Engineer, SRE (Machine Learning)
• Salary in India: 15-25 LPA (among the highest in ML due to scarcity)
• Job market: Extreme shortage of qualified MLOps engineers
• Next steps: Platform engineering, ML infrastructure, governance

Your Portfolio

You'll have:

1. A containerized ML model with API
2. A complete CI/CD pipeline
3. A production-ready system with monitoring

These are exactly what employers want.

Why MLOps is in High Demand

Every company training ML models needs someone to put them in production. But there are:

• Way more data scientists than MLOps engineers
• Way fewer people with DevOps + ML knowledge
• Way higher salaries for people who have both

This is your leverage point.

Course Structure

Ready to Bridge Models to Production?

Explore the MLOps Course →

MLOps specialists are in extremely high demand. Cohorts are small (max 20) due to hands-on infrastructure work.