MLOPS
This course covers the complete lifecycle of operationalizing ML models.
You’ll learn how to build, test, deploy, automate, optimize, and monitor ML models using modern tools such as Docker, Kubernetes, MLflow, Airflow, GitHub Actions, Kubeflow, and major cloud platforms.
The curriculum includes real-world workflows of data engineering + ML + CI/CD + cloud deployment — exactly how production-grade ML systems run in top companies.
By the end, learners will confidently build and deploy ML models at scale.
Learning Outcomes
MLOps Foundations
-
Understanding MLOps & its lifecycle
-
Difference between ML Engineer vs MLOps Engineer
-
ML workflow automation
-
Data versioning & model versioning
Version Control & Experiment Tracking
-
Git & GitHub for ML
-
DVC (Data Version Control)
-
MLflow tracking
-
Experiment management & reporting
Model Training Pipelines
-
Modular ML pipeline development
-
Feature engineering automation
-
Hyperparameter tuning
-
Reproducible experiments
Containerization & Virtualization
-
Docker basics for ML
-
Creating Docker images
-
Running ML workloads in containers
-
Managing dependencies
Orchestration Tools
-
Apache Airflow pipelines
-
Scheduling & workflow DAGs
-
ETL/ELT integration
-
Model retraining workflows
Deployment Techniques
-
Batch deployment
-
Real-time deployment
-
REST APIs using Flask / FASTAPI
-
CI/CD for model deployment
Cloud Computing for MLOps
-
AWS / GCP / Azure basics
-
Model deployment on cloud
-
Serverless deployment (Lambda / Cloud Functions)
-
Cloud storage & compute services
Kubernetes for MLOps
-
Docker + Kubernetes workflow
-
Pods, deployments, services
-
Scaling ML workloads
-
Kubeflow pipelines
Monitoring & Maintenance
-
Model drift detection
-
Data drift monitoring
-
Logging with Prometheus & Grafana
-
Automated retraining triggers
Security & Governance
-
ML model risk management
-
Secure access & API protection
-
Audit trails & compliance
-
Model registry best practices
Project Work
-
ML pipeline automation project
-
Dockerized ML model deployment
-
Airflow orchestration project
-
Cloud deployment project
-
Final MLOps Capstone: Automated ML pipeline + CI/CD + Monitoring
- Industry-style MLOps practices
- Complete deployment training
- Hands-on cloud + CI/CD
- Covers Docker, Kubernetes, Airflow, MLflow
- Real-world ML system design
- Job-ready curriculum
- ML Engineers
- Data Science beginners wanting production knowledge
- Developers wanting to learn MLOps
- Freshers preparing for ML/MLOps roles
- Anyone interested in model deployment & automation
- This course covers the complete lifecycle of operationalizing ML models. You’ll learn how to build, test, deploy, automate, optimize, and monitor ML models using modern tools such as Docker, Kubernetes, MLflow, Airflow, GitHub Actions, Kubeflow, and major cloud platforms. The curriculum includes real-world workflows of data engineering + ML + CI/CD + cloud deployment — exactly how production-grade ML systems run in top companies. By the end, learners will confidently build and deploy ML models at scale. Learning Outcomes MLOps Foundations Understanding MLOps & its lifecycle Difference between ML Engineer vs MLOps Engineer ML workflow automation Data versioning & model versioning Version Control & Experiment Tracking Git & GitHub for ML DVC (Data Version Control) MLflow tracking Experiment management & reporting Model Training Pipelines Modular ML pipeline development Feature engineering automation Hyperparameter tuning Reproducible experiments Containerization & Virtualization Docker basics for ML Creating Docker images Running ML workloads in containers Managing dependencies Orchestration Tools Apache Airflow pipelines Scheduling & workflow DAGs ETL/ELT integration Model retraining workflows Deployment Techniques Batch deployment Real-time deployment REST APIs using Flask / FASTAPI CI/CD for model deployment Cloud Computing for MLOps AWS / GCP / Azure basics Model deployment on cloud Serverless deployment (Lambda / Cloud Functions) Cloud storage & compute services Kubernetes for MLOps Docker + Kubernetes workflow Pods, deployments, services Scaling ML workloads Kubeflow pipelines Monitoring & Maintenance Model drift detection Data drift monitoring Logging with Prometheus & Grafana Automated retraining triggers Security & Governance ML model risk management Secure access & API protection Audit trails & compliance Model registry best practices Project Work ML pipeline automation project Dockerized ML model deployment Airflow orchestration project Cloud deployment project Final MLOps Capstone: Automated ML pipeline + CI/CD + Monitoring Features Industry-style MLOps practices Complete deployment training Hands-on cloud + CI/CD Covers Docker, Kubernetes, Airflow, MLflow Real-world ML system design Job-ready curriculum Target Audience ML Engineers Data Science beginners wanting production knowledge Developers wanting to learn MLOps Freshers preparing for ML/MLOps roles Anyone interested in model deployment & automation Requirements Basic Python Basic machine learning knowledge Laptop with internet No DevOps experience needed
- Basic machine learning knowledge
- Laptop with internet
- No DevOps experience needed
- 10 Sections
- 0 Lessons
- 11 Weeks
- Introduction to MLOps0
- Versioning & Experiment Tracking0
- Data & Model Pipelines0
- Containerization0
- Orchestration (Airflow)0
- Deployment0
- Cloud MLOps0
- Kubernetes & Kubeflow0
- Monitoring0
- Final Capstone Project0
You might be interested in
-
Beginner
-
482 Students
-
42 Lessons
-
Intermediate
-
375 Students
-
42 Lessons
-
Expert
-
338 Students
-
54 Lessons
-
Intermediate
-
356 Students
-
8 Lessons
