MLOps & Deployment | NetrAInsights

Level

Intermediate

Duration

3 Weeks

Hands-On Labs

Format

Self-paced

What You'll Learn

Bridge the gap between data science and software engineering. Learn to version data, automate training, monitor drift, and deploy models reliably using industry-standard MLOps tools and practices.

Experiment Tracking: MLflow, Weights & Biases
CI/CD for ML: GitHub Actions, DVC, automated retraining
Model Serving: FastAPI, BentoML, TorchServe
Containerization: Docker, Kubernetes, model packaging
Monitoring: Data drift detection, performance alerts
Feature Stores: Feast, Tecton, online/offline features

Course Modules

📊 Week 1: Experiment Tracking & Versioning▼

MLOps maturity model
Experiment tracking with MLflow
Data versioning with DVC
Model registry and staging workflows
Reproducible pipelines
Lab 1: Track experiments with MLflow
Lab 2: Version data with DVC and Git
Lab 3: Build reproducible training pipeline
Lab 4: Model registry with MLflow

🐳 Week 2: Containerization & CI/CD▼

Dockerize ML models
REST API with FastAPI
GitHub Actions for ML pipelines
Kubernetes basics for ML workloads
Model serving with BentoML
Lab 5: Dockerize a Flask ML API
Lab 6: Build FastAPI inference server
Lab 7: CI/CD with GitHub Actions
Lab 8: Deploy to Kubernetes cluster

📈 Week 3: Monitoring & Production Best Practices▼

Data and concept drift detection
Model performance monitoring
A/B testing in production
Shadow mode deployment
Feature stores (Feast)
Lab 9: Detect data drift with Evidently
Lab 10: Set up Prometheus + Grafana alerts
Lab 11: A/B test two model versions
Lab 12 (Capstone): Full MLOps pipeline from training to monitoring

Prerequisites

Python (intermediate)
Basic ML knowledge
Git familiarity
Docker basics helpful but not required

Who Should Take This?

ML Engineers moving to production
Data Scientists owning their deployment
DevOps Engineers supporting ML teams
Platform Engineers building ML infrastructure

Tools & Tech Stack

Python, FastAPI, Docker, Kubernetes
MLflow, DVC, Weights & Biases
GitHub Actions, BentoML, TorchServe
Evidently AI, Prometheus, Grafana
Feast (feature store)

Ready to Start?

Stop letting models die in Jupyter notebooks. Deploy with confidence and monitor with precision.

📧 Enroll Now

⚙️ MLOps & Deployment