MLOps Courses and Certifications

MLOps with AWS - Bootcamp - Zero to Hero Series

1131 ratings at Udemy

Empower Your MLOps Journey: AWS AI/ML Mastery - From Notebook to Production Operation with Expert Guidance - MLOps 2024

• Udemy
• 1 day 13 hours 55 minutes
• Self-Paced
• Paid Course

Credential

Machine Learning Engineering for Production (MLOps)

Develop production-ready ML systems: conceptualize, build, and maintain integrated solutions that handle evolving data, operate continuously, and balance cost with performance for real-world AI applications.

• Coursera
• 17 weeks, 6 hours a week
• Self-Paced
• Paid Course
• 4 courses

Pragmatic AI Labs

Python Fundamentals for MLOps

Master Python for Seamless MLOps: From Fundamentals to Automation and Workflow Efficiency.

• edX
• 5 weeks, 3-6 hours/week
• Self-Paced
• Free Online Course (Audit)

Pragmatic AI Labs

DevOps, DataOps, MLOps

Streamline AI Operations: Leverage DevOps, DataOps, and MLOps for End-to-End Machine Learning Solutions.

• edX
• 4 weeks, 3-6 hours/week
• Self-Paced
• Free Online Course (Audit)

Coursera

Operationalizing ML Models: MLOps for Scalable AI

Master MLOps practices to transform ML prototypes into scalable, production-ready systems using Docker, MLflow, and Kubernetes. Automate deployments, monitor performance, and optimize AI infrastructure for robust applications.

• Coursera
• 3 hours 40 minutes
• Self-Paced
• Paid Course

Learn MLOps for Machine Learning

Discover how to streamline ML workflows with DVC, MLFlow, and AWS tools for tracking experiments, versioning data, and automating model deployment processes.

• Coursera
• 6 hours 39 minutes
• Self-Paced
• Paid Course

MLOps and responsible AI practices

Master MLOps and responsible AI practices on Azure, implementing CI/CD pipelines, monitoring, and ethical frameworks for production-ready generative models.

• Coursera
• 21 hours 50 minutes
• Self-Paced
• Paid Course

Packt

AI Agents and MLOps for Production-Ready AI

Master AI agents and MLOps to build production-ready systems using AutoGen, CrewAI, Docker, and Kubernetes for scalable deployment and automation workflows.

• Coursera
• 5 hours 28 minutes
• Self-Paced
• Paid Course

Packt

DevOps to MLOps Bootcamp– Build & Deploy ML Systems

Master the complete MLOps lifecycle from development to production deployment using Docker, Kubernetes, MLflow, and GitHub Actions through hands-on house price prediction project.

• Coursera
• 14 hours 22 minutes
• Self-Paced
• Paid Course

KodeKloud Credential

Hands-On MLOps Fundamentals for ML Engineers

Master the complete machine learning lifecycle from data ingestion to production deployment through hands-on projects, gaining job-ready MLOps expertise for scalable ML systems.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Pragmatic AI Labs

MLOps Platforms: Amazon SageMaker and Azure ML

Elevate Your MLOps Game: Master AWS SageMaker and Azure ML for Production-Ready AI Solutions.

• edX
• 4 weeks, 3-6 hours/week
• Self-Paced
• Free Online Course (Audit)

Pragmatic AI Labs

Cloud Machine Learning Engineering and MLOps

Unleash the power of machine learning with engineering best practices, AutoML, and emerging technologies.

• edX
• 4 weeks, 3-6 hours/week
• Self-Paced
• Free Online Course (Audit)

Pragmatic AI Labs

Production Governance and MLOps on Databricks

Master Databricks governance, Unity Catalog, CI/CD workflows, and MLOps practices to build secure, reliable data pipelines and AI applications with hands-on tools and real-world code.

• Coursera
• 6 hours 59 minutes
• Self-Paced
• Paid Course

Coursera Credential

Gradient to Production: MLOps & Model Serving

Master the full MLOps stack—from data pipelines to model deployment—using FastAPI, Docker, Kubernetes, and Airflow, and learn to ship, monitor, and maintain ML systems in production.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 15 courses

Board Infinity

MLOps and LLMOps: Deploying and Scaling AI in Production

Master deploying and scaling AI systems in production—covering MLOps, LLMOps, CI/CD pipelines, RAG, Kubernetes, observability, and automated evaluation with real-world tools.

• Coursera
• 17 hours 19 minutes
• Self-Paced
• Paid Course