MLOps Courses and Certifications

Career Certificate

Machine Learning Operations (MLOps) on Google Cloud

513 ratings at Coursera

Bridge ML prototypes to production on Google Cloud: manage features with Vertex AI Feature Store, evaluate predictive and generative models, and orchestrate pipelines with the Kubeflow SDK.

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

Machine Learning Operations (MLOps): Getting Started

492 ratings at Coursera

Learn MLOps practices for production ML on Google Cloud: DevOps applied to ML, the three phases of the ML lifecycle, process automation, and Vertex AI.

• Coursera
• 4 weeks, 1 hour a week
• Self-Paced
• Paid Course

Complete MLOps Bootcamp With 10+ End To End ML Projects

3842 ratings at Udemy

End-to-End MLOps Bootcamp: Build, Deploy, and Automate ML with Data Science Projects

• Udemy
• 2 days 2 hours 46 minutes
• Self-Paced
• Paid Course

MLOps Zero to Hero

2280 ratings at Udemy

Learn Production-Grade MLOps using DVC, MLFlow, AWS, Docker, Kubernetes, KServe, SageMaker and Kubeflow.

• Udemy
• 12 hours 20 minutes
• Self-Paced
• Paid Course

MLOps with AWS - Bootcamp - Zero to Hero Series

1213 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

Machine Learning Operations (MLOps) with Vertex AI: Manage Features

20 ratings at Coursera

Manage ML features on Google Cloud: explore Vertex AI Feature Store's key capabilities and practice streaming ingestion at the SDK layer for production ML systems.

• Coursera
• 4 weeks, 1 hour a week
• Self-Paced
• Paid Course

MLOps for Scaling TinyML

8 ratings at edX

This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning). Learners explore best practices to deploy, monitor, and maintain (tiny) Machi

• edX
• 7 weeks, 2-4 hours a week
• Self-Paced
• Free Online Course (Audit)

Career Certificate

Machine Learning Engineering for Production (MLOps)

Build and maintain production machine learning systems: design data lifecycles, modeling pipelines, and model deployment that keep running on continuously evolving data at minimum cost.

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

Coursera Career Certificate

Gradient to Production: MLOps & Model Serving

5 ratings at Coursera

Ship models beyond the notebook: build data pipelines, testable Python packages, containerized deployments with Docker and Kubernetes, FastAPI inference services, and drift monitoring.

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

MLOps and responsible AI practices

Take generative AI models from development to production on Azure: build CI/CD pipelines, manage lifecycles with Azure ML and MLflow, monitor costs, and apply Responsible AI practices.

• Coursera
• 4 weeks, 5 hours a week
• Self-Paced
• Paid Course

Packt

AI Agents and MLOps for Production-Ready AI

Build autonomous agents with AutoGen, IBM Bee, LangGraph, CrewAI, and AutoGPT, then operationalize models through MLOps pipelines with Git, Docker, Kubernetes, and cloud platforms.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Operationalizing ML Models: MLOps for Scalable AI

Turn ML prototypes into production systems: design MLOps workflows, automate CI/CD deployments, monitor data drift, and scale infrastructure with Docker, MLflow, and Kubernetes.

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

KodeKloud Career Certificate

Hands-On MLOps Fundamentals for ML Engineers

Move from DevOps to MLOps: handle data ingestion, track model development, and deploy monitored ML models to production via an automated insurance claim processing project.

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

Packt

DevOps to MLOps Bootcamp– Build & Deploy ML Systems

Build and deploy an end-to-end ML system: track experiments with MLflow, package models with FastAPI and Docker, and automate delivery via GitHub Actions, Kubernetes, and ArgoCD.

• Coursera
• 8 weeks, 2 hours a week
• Self-Paced
• Paid Course

Board Infinity

MLOps and LLMOps: Deploying and Scaling AI in Production

Design, deploy, and scale production ML and LLM systems with experiment tracking, ML-specific CI/CD, Kubernetes serving, canary and blue-green rollouts, and drift monitoring.

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