Comprehensive List of ALL MLOps Courses (2025)

Educational Platforms

Coursera

1. MLOps | Machine Learning Operations (Duke University)

   • Skills: MLOps (Machine Learning Operations), Pandas (Python Package), AWS SageMaker, NumPy

   • Level: Advanced

   • Duration: 3-6 Months

   • Format: Specialization

   • Rating: 4.1 (422 reviews)

   • URL: https://www.coursera.org/specializations/mlops-machine-learning-duke

2. DevOps, DataOps, MLOps (Duke University)

   • Skills: MLOps (Machine Learning Operations), Data Ethics, Artificial Intelligence, Machine Learning

   • Level: Advanced

   • Duration: 1-3 Months

   • Format: Course

   • Rating: 4.1 (155 reviews)

   • URL: https://www.coursera.org/learn/devops-dataops-mlops-duke

3. Machine Learning Operations (MLOps) : Getting Started (Google Cloud)

   • Skills: MLOps (Machine Learning Operations), Google Cloud Platform, Cloud Management, DevOps

   • Level: Intermediate

   • Duration: 1-4 Weeks

   • Format: Course

   • Rating: 4.1 (458 reviews)

   • URL: https://www.coursera.org/learn/machine-learning-operations-mlops-getting-started

4. MLOps Platforms: Amazon SageMaker and Azure ML (Duke University)

   • Skills: AWS SageMaker, MLOps (Machine Learning Operations), Microsoft Azure, Exploratory Data Analysis

   • Level: Advanced

   • Duration: 1-3 Months

   • Format: Course

   • Rating: 3.6 (46 reviews)

   • URL: https://www.coursera.org/learn/mlops-aws-azure-duke

5. MLOps Tools: MLflow and Hugging Face (Duke University)

   • Skills: MLOps (Machine Learning Operations), Application Deployment, Containerization, CI/CD

   • Level: Advanced

   • Duration: 1-4 Weeks

   • Format: Course

   • Rating: 3.7 (49 reviews)

   • URL: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke

6. Python Essentials for MLOps (Duke University)

   • Skills: Pandas (Python Package), MLOps (Machine Learning Operations), NumPy, Unit Testing

   • Level: Intermediate

   • Duration: 1-3 Months

   • Format: Course

   • Rating: 4.2 (246 reviews)

   • URL: https://www.coursera.org/learn/python-essentials-mlops-duke

7. Machine Learning in Production (DeepLearning.AI)

   • Skills: MLOps (Machine Learning Operations), Application Deployment, Continuous Deployment

   • Level: Intermediate

   • Duration: 1-4 Weeks

   • Format: Course

   • Rating: 4.8 (3.2K reviews)

   • URL: https://www.coursera.org/learn/machine-learning-in-production

8. Microsoft AI & ML Engineering (Microsoft)

   • Skills: Unsupervised Learning, Generative AI, Data Management, Natural Language Processing

   • Level: Intermediate

   • Duration: 3-6 Months

   • Format: Professional Certificate

   • Rating: 4.6 (95 reviews)

   • URL: https://www.coursera.org/professional-certificates/microsoft-ai-ml-engineering

9. **Large Language Model Operations (LLMOps) ** (Duke University)

   • Skills: Databricks, Generative AI, Performance Analysis, Apache Airflow, Workflow Management

   • Level: Beginner

   • Duration: 3-6 Months

   • Format: Specialization

   • Rating: 4.5 (161 reviews)

   • URL: https://www.coursera.org/specializations/large-language-model-operations-llmops

edX

1. MLOps for Scaling TinyML (Harvard University)

   • Provider: HarvardX

   • Duration: 7 weeks (2-4 hours per week)

   • Level: Not specified

   • Format: Course

   • Rating: 4.6 stars (8 ratings)

   • Description: This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning).

   • URL: https://www.edx.org/learn/tinyml/harvard-university-mlops-for-scaling-tinyml

2. **Machine Learning Operations with Microsoft Azure (MLOps) ** (Statistics.comX)

   • Format: Professional Certificate

   • Description: Focuses on MLOps implementation using Microsoft Azure platform.

   • URL: https://www.edx.org/certificates/professional-certificate/statisticscomx-machine-learning-operations-with-microsoft-azure-mlops-with-azure

3. **Machine Learning Operations with Amazon Web Services (MLOps) ** (Statistics.comX)

   • Format: Professional Certificate

   • Description: Focuses on MLOps implementation using AWS platform.

   • URL: https://www.edx.org/certificates/professional-certificate/statisticscomx-machine-learning-operations-with-amazon-web-services-mlops-with-aws

4. **Machine Learning Operations with Google Cloud Platform (MLOps) ** (Statistics.comX)

   • Format: Professional Certificate

   • Description: Focuses on MLOps implementation using Google Cloud Platform.

   • URL: https://www.edx.org/certificates/professional-certificate/statisticscomx-machine-learning-operations-using-google-cloud-platform-mlops-with-gcp

5. MLOps1 (Azure) : Deploying AI & ML Models in Production (Statistics.comX)

   • Format: Course

   • Description: Focuses on deploying AI and ML models in production using Azure.

   • URL: https://www.edx.org/learn/machine-learning/statistics-com-mlops1-azure-deploying-ai-ml-models-in-production

6. MLOps1 (AWS) : Deploying AI & ML Models in Production (Statistics.comX)

   • Format: Course

   • Description: Focuses on deploying AI and ML models in production using AWS.

   • URL: https://www.edx.org/learn/machine-learning/statistics-com-mlops1-aws-deploying-ai-ml-models-in-production

Udemy

1. Complete MLOps Bootcamp With 10+ End To End ML Projects

   • Description: Comprehensive bootcamp covering multiple MLOps projects

   • URL: https://www.udemy.com/topic/mlops/

2. Introducing MLOps: From Model Development to Deployment

   • Description: A practical guide to building, automating, and scaling Machine Learning pipelines

   • Content includes: Introduction to MLOps, Data Science to Production Pipeline, Infrastructure for MLOps

   • Last updated: March 21, 2025

   • URL: https://www.udemy.com/course/mastering-mlops-from-model-development-to-deployment/

3. Mastering MLOps: Complete course for ML Operations

   • Topics covered:

     • MLOps fundamentals

     • Model versioning with MLFlow

     • Data versioning

     • Auto-ML and Low-code MLOps

     • Explainability, Auditability, and Interpretable machine learning

   • URL: https://www.udemy.com/course/mastering-mlops-complete-course-for-ml-operations/

4. MLOps for Beginners

   • Format: Free tutorial

   • Description: Learn the building blocks of MLOps, best practices and tools that facilitate rapid, safe, and efficient development

   • URL: https://www.udemy.com/course/mlops-for-beginners/

5. Beginner to Advanced MLOps on GCP-CI/CD, Kubernetes Jenkins

   • Description: Covers a wide range of technologies and tools essential for building, deploying, and automating ML

   • Focus: Google Cloud Platform, CI/CD, Kubernetes

   • Last updated: March 11, 2025

   • URL: https://www.udemy.com/course/mastering-advanced-mlops-on-gcp-cicd-kubernetes-kubeflow/

6. LLMOps Masterclass 2025 - Generative AI - MLOps - AIOps

   • Description: Comprehensive course on Generative AI with focus on LLMOps

   • Content: From fundamentals to deploying advanced applications

   • URL: https://www.udemy.com/course/llmops-masterclass-generative-ai-mlops-aiops/

7. Azure Machine Learning & MLOps: Beginner to Advance

   • Focus: MLOps implementation on Azure platform

   • Level: Beginner to Advanced

   • URL: Not directly available in search results

8. MLOps Fundamentals - Learn MLOps Concepts with Azure demo

   • Focus: Azure-based MLOps implementation with practical demonstrations

   • URL: Not directly available in search results

Pluralsight

1. MLOps (Machine Learning Operations)  Fundamentals

   • Description: This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

   • Platform focus: Google Cloud

   • Last updated: December 1, 2022

   • URL: https://www.pluralsight.com/courses/mlops-machine-learning-operations-fundamentals-6

2. **Demystifying Machine Learning Operations (MLOps) **

   • Description: This course teaches the main concerns and issues to consider while developing a machine learning model and after deploying it.

   • Last updated: November 23, 2020

   • URL: https://www.pluralsight.com/courses/demystifying-machine-learning-operations

3. Machine Learning Operations (MLOps)  for Generative AI

   • Description: This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams working with generative AI.

   • Focus: Generative AI

   • Last updated: June 25, 2024

   • URL: https://www.pluralsight.com/courses/machine-learning-operations-mlops-generative-ai

4. Introduction to MLOps on Azure

   • Description: Tackles real-world scenarios through lessons and labs, teaching how to ingest and prepare data, apply algorithms, and score.

   • Platform focus: Microsoft Azure

   • Last updated: July 30, 2024

   • URL: https://www.pluralsight.com/courses/introduction-to-mlops-on-azure

5. Machine Learning Operations (MLOps) : Getting Started

   • Description: This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

   • Platform focus: Google Cloud

   • URL: https://www.pluralsight.com/courses/machine-learning-operations-mlops-getting-started-1

6. Machine Learning Operations (MLOps)  with Vertex AI: Model Evaluation

   • Description: This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models.

   • Platform focus: Google Vertex AI

   • Last updated: June 28, 2024

   • URL: https://www.pluralsight.com/courses/machine-learning-operations-mlops-vertex-ai-model-evaluation

7. AWS Authorized Training Course - MLOps Engineering on AWS

   • Description: Covers managing data for MLOps, version control of ML models, and code repositories in ML.

   • Platform focus: Amazon Web Services (AWS)

   • URL: https://www.pluralsight.com/professional-services/cloud/aws-authorized-training-course---mlops-engineering-on-aws

8. Manage Your End-to-end Machine Learning Lifecycle with MLOps

   • Description: Shows how to get to production faster with MLOps workflows.

   • Last updated: August 26, 2020

   • URL: https://www.pluralsight.com/courses/microsoft-ignite-session-74

LinkedIn Learning

1. MLOps Essentials: Model Development and Integration

   • Instructor: Kumaran Ponnambalam

   • Description: Provides structure to machine learning projects and helps them succeed over the long run

   • Last updated: September 16, 2022

   • URL: https://www.linkedin.com/learning/mlops-essentials-model-development-and-integration

2. MLOps Essentials: Model Deployment and Monitoring

   • Description: Learn how to deploy and monitor machine learning models to deliver scalable, reliable ML products and services

   • Last updated: October 7, 2022

   • URL: https://www.linkedin.com/learning/mlops-essentials-model-deployment-and-monitoring

3. MLOps Essentials: Monitoring Model Drift and Bias

   • Description: Learn about the growing field of MLOps and the modeling techniques used to monitor model drift and bias

   • Last updated: October 6, 2023

   • URL: https://www.linkedin.com/learning/mlops-essentials-monitoring-model-drift-and-bias

4. MLOps with Databricks

   • Description: Explore the main components and principles required to deploy machine learning models to production on Databricks

   • Last updated: December 19, 2024

   • URL: https://www.linkedin.com/learning/mlops-with-databricks

5. MLOps Tools: MLflow and Hugging Face

   • Description: Learn how to master MLflow and Hugging Face, two powerful open-source platforms for MLOps

   • Last updated: September 25, 2024

   • URL: https://www.linkedin.com/learning/mlops-tools-mlflow-and-hugging-face

6. Essentials of MLOps with Azure

   • Description: Learn how to get started with MLflow and MLflow Tracking using Azure Databricks

   • Last updated: September 8, 2022

   • URL: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-2-databricks-mlflow-and-mlflow-tracking

7. Full-stack Deep Learning with Python

   • Description: Includes sections on MLOps and MLflow as part of the full-stack deep learning workflow

   • Last updated: February 6, 2024

   • URL: https://www.linkedin.com/learning/full-stack-deep-learning-with-python

8. Scaling Your AI/ML Practices with MLOps and Azure Machine Learning

   • Description: Learn how to utilize AzureML MLOps capabilities to streamline the process of moving ML experiments from development to production

   • Last updated: January 19, 2024

   • URL: https://www.linkedin.com/learning/ai-show-scaling-your-ai-ml-practices-with-mlops-and-azure-machine-learning

DataCamp

1. MLOps Concepts

   • Description: Delve deep into the core concepts of MLOps and learn how it can be applied to scale and automate ML projects effectively

   • URL: https://www.datacamp.com/courses/mlops-concepts

2. MLOps Fundamentals (Track)

   • Description: A comprehensive track covering the fundamentals of MLOps

   • URL: https://www.datacamp.com/tracks/mlops-fundamentals

3. MLOps Deployment and LifeCycling

   • Description: Covers the modern MLOps framework, lifecycle and deployment of machine learning models. Learn to write ML code, deploy ML models

   • URL: https://www.datacamp.com/courses/mlops-deployment-and-life-cycling

4. Fully Automated MLOps

   • Description: Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time

   • URL: https://www.datacamp.com/courses/fully-automated-mlops

5. MLOps for Business

   • Description: Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications

   • URL: https://www.datacamp.com/courses/mlops-for-business

Udacity

1. Machine Learning DevOps Engineer Nanodegree

   • Description: This program covers key skills like writing production-ready code, creating reproducible workflows, and building automated deployment pipelines

   • Format: Nanodegree program

   • Content: The program consists of 4 key courses covering various aspects of MLOps

   • URL: https://www.udacity.com/course/machine-learning-dev-ops-engineer-nanodegree--nd0821

2. **Building Real-World Applications With Large Language Models (LLMOps) **

   • Description: Teaches everything needed to build real-world, production software with large language models

   • Partnership: Developed in partnership with Comet ML

   • Format: Self-paced course

   • Last updated: January 2024

   • URL: https://www.udacity.com/course/building-real-world-applications-with-large-language-models--cd13455

Cloud Provider Learning Platforms

AWS (Amazon Web Services)

1. MLOps Engineering on AWS

   • Description: This course builds upon and extends the DevOps methodology prevalent in software development to building, training, and deploying ML models

   • Format: Instructor-Led Training (3 days)

   • Level: Intermediate

   • URL: https://aws.amazon.com/training/classroom/mlops-engineering-on-aws/

2. AWS MLOps Workshops

   • Description: Hands-on workshops designed to teach or introduce practical MLOps skills on AWS

   • Format: Self-paced workshops

   • URL: https://workshops.aws/categories/MLOps

Google Cloud Platform (GCP)

1. Machine Learning Operations (MLOps): Getting Started

   • Description: This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud

   • Format: Online course

   • URL: https://www.cloudskillsboost.google/course_templates/158

2. Machine Learning Engineer Learning Path

   • Description: A comprehensive learning path that includes MLOps training for Google Cloud

   • Format: Learning path with multiple courses

   • URL: https://www.cloudskillsboost.google/paths/17

Microsoft Azure

1. End-to-end machine learning operations (MLOps)  with Azure Machine Learning

   • Description: Learn how to implement key concepts like source control, automation, and CI/CD to build an end-to-end MLOps solution

   • Format: Learning path

   • URL: https://learn.microsoft.com/en-us/training/paths/build-first-machine-operations-workflow/

2. **Introduction to machine learning operations (MLOps) **

   • Description: Learn about which DevOps principles help in scaling a machine learning project

   • Format: Learning path

   • URL: https://learn.microsoft.com/en-us/training/paths/introduction-machine-learn-operations/

3. **Azure Machine Learning and MLOps (DW-202) **

   • Description: In-depth exploration of Azure Machine Learning and its comprehensive tools for automating, training, deploying, and managing ML models

   • Format: Instructor-led course

   • URL: https://www.lumifywork.com/en-au/courses/microsoft-dw-202-azure-machine-learning-and-mlops/

Universities and Academic Institutions

1. MLOps | Machine Learning Operations Specialization (Duke University)

   • Format: Specialization (Coursera)

   • Description: Become a Machine Learning Engineer with MLOps skills

   • URL: https://www.coursera.org/specializations/mlops-machine-learning-duke

2. MLOps – Machine Learning Operations (Iowa State University)

   • Format: Online course

   • Description: Hands-on course covering essential topics like MLflow, Data Pipelines, RestAPI development, and containerization

   • URL: https://iowastateonline.iastate.edu/programs-and-courses/artificial-intelligence/mlops-machine-learning-operations/

3. AAI 540 - Machine Learning Operations (University of San Diego)

   • Format: Course (part of degree program)

   • Description: Introduces students to the key concepts of MLOps and a holistic method of designing suitable ML systems

   • URL: https://onlinedegrees.sandiego.edu/classes/applications-of-artificial-intelligence-and-big-data-maai-540/

4. Machine Learning Operations Certificate (University of San Francisco)

   • Format: 7-week certificate program

   • Description: Designed for data scientists, machine learning engineers, and software developers eager to bridge the gap between developing ML models and deploying them at scale

   • URL: https://www.usfca.edu/data-institute/certificates/machine-learning-operations

5. MLOps for Scaling TinyML (Harvard University)

   • Format: Course

   • Description: Introduces MLOps through the lens of TinyML (Tiny Machine Learning) to help deploy and monitor applications responsibly at scale

   • URL: https://pll.harvard.edu/course/mlops-scaling-tinyml

6. AI/ML Ops - 635.603 (Johns Hopkins University)

   • Format: Course

   • Description: Students learn foundational AI/ML Ops concepts, including frameworks for building and deploying AI/ML models, infrastructure optimization for machine learning

   • URL: https://ep.jhu.edu/courses/635603-ai-ml-ops/

7. Introduction to MLOps: Bridging Machine Learning and Operations (Carnegie Mellon University - SEI)

   • Format: Educational resource/blog

   • Description: Covers MLOps practices that aim to streamline and automate the lifecycle of ML models in production environments

   • URL: https://insights.sei.cmu.edu/blog/introduction-to-mlops-bridging-machine-learning-and-operations/

Industry Providers

Tech Company Certifications

1. Intel® Certified Developer - MLOps Professional

   • Provider: Intel

   • Description: Teaches best practices for building compute-aware AI solutions

   • URL: https://www.intel.com/content/www/us/en/developer/certification/mlops.html

2. Google Professional Machine Learning Engineer

   • Provider: Google Cloud

   • Description: Certification for professionals who design, build, and productionize ML models to solve business challenges

   • URL: https://cloud.google.com/learn/certification/machine-learning-engineer

3. AWS Certified Machine Learning - Specialty

   • Provider: Amazon Web Services

   • Description: Validates expertise in designing, implementing, deploying, and maintaining machine learning solutions

   • URL: https://aws.amazon.com/certification/certified-machine-learning-specialty/

4. Microsoft Certified: Azure AI Engineer Associate

   • Provider: Microsoft

   • Description: Validates skills in using Azure AI services to build and deploy AI solutions

   • URL: https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/

5. Databricks Certified Machine Learning Professional

   • Provider: Databricks

   • Description: Validates expertise in using Databricks for machine learning workflows

   • URL: https://www.databricks.com/learn/certification/machine-learning-professional

Industry Organizations

1. IABAC Certified MLOps Engineer

   • Provider: International Association of Business Analytics Certification (IABAC)

   • Description: Certification program that validates MLOps skills and expertise

   • URL: https://iabac.org/data-science-certification/certified-mlops-engineer

Vendor-Specific MLOps Training

1. Databricks MLOps Training

   • Provider: Databricks

   • Description: Training on MLOps architecture, CI/CD/CM/CT techniques, and automation patterns

   • URL: https://www.databricks.com/training/catalog/machine-learning-operations-2403

2. DataRobot MLOps

   • Provider: DataRobot

   • Description: Training on deploying, monitoring, and managing machine learning models in production

   • URL: https://www.datarobot.com/platform/mlops/

Courses by Experience Level

Beginner Level

1. MLOps for Beginners (Udemy)

   • Format: Free tutorial

   • URL: https://www.udemy.com/course/mlops-for-beginners/

2. **Large Language Model Operations (LLMOps) ** (Duke University via Coursera)

   • Level: Beginner

   • Duration: 3-6 Months

   • Format: Specialization

   • URL: https://www.coursera.org/specializations/large-language-model-operations-llmops

3. Azure Machine Learning & MLOps: Beginner to Advance (Udemy)

   • Focus: MLOps implementation on Azure platform

   • Level: Beginner to Advanced

Intermediate Level

1. Machine Learning Operations (MLOps): Getting Started (Google Cloud via Coursera)

   • Level: Intermediate

   • Duration: 1-4 Weeks

   • URL: https://www.coursera.org/learn/machine-learning-operations-mlops-getting-started

2. Python Essentials for MLOps (Duke University via Coursera)

   • Level: Intermediate

   • Duration: 1-3 Months

   • URL: https://www.coursera.org/learn/python-essentials-mlops-duke

3. Machine Learning in Production (DeepLearning.AI via Coursera)

   • Level: Intermediate

   • Duration: 1-4 Weeks

   • URL: https://www.coursera.org/learn/machine-learning-in-production

4. Microsoft AI & ML Engineering (Microsoft via Coursera)

   • Level: Intermediate

   • Duration: 3-6 Months

   • URL: https://www.coursera.org/professional-certificates/microsoft-ai-ml-engineering

5. MLOps Engineering on AWS

   • Level: Intermediate

   • Format: Instructor-Led Training (3 days)

   • URL: https://aws.amazon.com/training/classroom/mlops-engineering-on-aws/

Advanced Level

1. MLOps | Machine Learning Operations (Duke University via Coursera)

   • Level: Advanced

   • Duration: 3-6 Months

   • URL: https://www.coursera.org/specializations/mlops-machine-learning-duke

2. DevOps, DataOps, MLOps (Duke University via Coursera)

   • Level: Advanced

   • Duration: 1-3 Months

   • URL: https://www.coursera.org/learn/devops-dataops-mlops-duke

3. MLOps Platforms: Amazon SageMaker and Azure ML (Duke University via Coursera)

   • Level: Advanced

   • Duration: 1-3 Months

   • URL: https://www.coursera.org/learn/mlops-aws-azure-duke

4. MLOps Tools: MLflow and Hugging Face (Duke University via Coursera)

   • Level: Advanced

   • Duration: 1-4 Weeks

   • URL: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke

Courses by Duration

Short Courses (Less than 4 weeks)

1. Machine Learning Operations (MLOps): Getting Started (Google Cloud via Coursera)

   • Duration: 1-4 Weeks

   • URL: https://www.coursera.org/learn/machine-learning-operations-mlops-getting-started

2. MLOps Tools: MLflow and Hugging Face (Duke University via Coursera)

   • Duration: 1-4 Weeks

   • URL: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke

3. Machine Learning in Production (DeepLearning.AI via Coursera)

   • Duration: 1-4 Weeks

   • URL: https://www.coursera.org/learn/machine-learning-in-production

4. MLOps Engineering on AWS

   • Duration: 3 days

   • URL: https://aws.amazon.com/training/classroom/mlops-engineering-on-aws/

Medium Courses (1-3 months)

1. DevOps, DataOps, MLOps (Duke University via Coursera)

   • Duration: 1-3 Months

   • URL: https://www.coursera.org/learn/devops-dataops-mlops-duke

2. MLOps Platforms: Amazon SageMaker and Azure ML (Duke University via Coursera)

   • Duration: 1-3 Months

   • URL: https://www.coursera.org/learn/mlops-aws-azure-duke

3. Python Essentials for MLOps (Duke University via Coursera)

   • Duration: 1-3 Months

   • URL: https://www.coursera.org/learn/python-essentials-mlops-duke

4. Machine Learning Operations Certificate (University of San Francisco)

   • Duration: 7 weeks

   • URL: https://www.usfca.edu/data-institute/certificates/machine-learning-operations

5. MLOps for Scaling TinyML (Harvard University via edX)

   • Duration: 7 weeks (2-4 hours per week)

   • URL: https://www.edx.org/learn/tinyml/harvard-university-mlops-for-scaling-tinyml

Long Courses (3+ months)

1. MLOps | Machine Learning Operations (Duke University via Coursera)

   • Duration: 3-6 Months

   • URL: https://www.coursera.org/specializations/mlops-machine-learning-duke

2. Microsoft AI & ML Engineering (Microsoft via Coursera)

   • Duration: 3-6 Months

   • URL: https://www.coursera.org/professional-certificates/microsoft-ai-ml-engineering

3. **Large Language Model Operations (LLMOps) ** (Duke University via Coursera)

   • Duration: 3-6 Months

   • URL: https://www.coursera.org/specializations/large-language-model-operations-llmops

4. Machine Learning DevOps Engineer Nanodegree (Udacity)

   • Duration: Typically 3+ months

   • URL: https://www.udacity.com/course/machine-learning-dev-ops-engineer-nanodegree--nd0821

Courses by Focus Area

General MLOps

1. MLOps | Machine Learning Operations (Duke University via Coursera)

   • URL: https://www.coursera.org/specializations/mlops-machine-learning-duke

2. DevOps, DataOps, MLOps (Duke University via Coursera)

   • URL: https://www.coursera.org/learn/devops-dataops-mlops-duke

3. MLOps Concepts (DataCamp)

   • URL: https://www.datacamp.com/courses/mlops-concepts

4. MLOps Fundamentals (DataCamp)

   • URL: https://www.datacamp.com/tracks/mlops-fundamentals

5. **Demystifying Machine Learning Operations (MLOps) ** (Pluralsight)

   • URL: https://www.pluralsight.com/courses/demystifying-machine-learning-operations

6. Machine Learning DevOps Engineer Nanodegree (Udacity)

   • URL: https://www.udacity.com/course/machine-learning-dev-ops-engineer-nanodegree--nd0821

Cloud-Specific MLOps

1. MLOps Platforms: Amazon SageMaker and Azure ML (Duke University via Coursera)

   • URL: https://www.coursera.org/learn/mlops-aws-azure-duke

2. Machine Learning Operations (MLOps) : Getting Started (Google Cloud via Coursera)

   • URL: https://www.coursera.org/learn/machine-learning-operations-mlops-getting-started

3. Introduction to MLOps on Azure (Pluralsight)

   • URL: https://www.pluralsight.com/courses/introduction-to-mlops-on-azure

4. **Machine Learning Operations with Microsoft Azure (MLOps) ** (Statistics.comX via edX)

   • URL: https://www.edx.org/certificates/professional-certificate/statisticscomx-machine-learning-operations-with-microsoft-azure-mlops-with-azure

5. **Machine Learning Operations with Amazon Web Services (MLOps) ** (Statistics.comX via edX)

   • URL: https://www.edx.org/certificates/professional-certificate/statisticscomx-machine-learning-operations-with-amazon-web-services-mlops-with-aws

6. **Machine Learning Operations with Google Cloud Platform (MLOps) ** (Statistics.comX via edX)

   • URL: https://www.edx.org/certificates/professional-certificate/statisticscomx-machine-learning-operations-using-google-cloud-platform-mlops-with-gcp

7. MLOps Engineering on AWS

   • URL: https://aws.amazon.com/training/classroom/mlops-engineering-on-aws/

8. End-to-end machine learning operations (MLOps)  with Azure Machine Learning

   • URL: https://learn.microsoft.com/en-us/training/paths/build-first-machine-operations-workflow/

LLMOps (Large Language Model Operations)

1. Large Language Model Operations (LLMOps) (Duke University via Coursera)

   • URL: https://www.coursera.org/specializations/large-language-model-operations-llmops

2. LLMOps Masterclass 2025 - Generative AI - MLOps - AIOps (Udemy)

   • URL: https://www.udemy.com/course/llmops-masterclass-generative-ai-mlops-aiops/

3. **Building Real-World Applications With Large Language Models (LLMOps) ** (Udacity)

   • URL: https://www.udacity.com/course/building-real-world-applications-with-large-language-models--cd13455

4. Machine Learning Operations (MLOps)  for Generative AI (Pluralsight)

   • URL: https://www.pluralsight.com/courses/machine-learning-operations-mlops-generative-ai

TinyML Operations

1. MLOps for Scaling TinyML (Harvard University via edX)

   • URL: https://www.edx.org/learn/tinyml/harvard-university-mlops-for-scaling-tinyml

2. MLOps for Scaling TinyML (Harvard University)

   • URL: https://pll.harvard.edu/course/mlops-scaling-tinyml

MLOps Tools and Frameworks

1. MLOps Tools: MLflow and Hugging Face (Duke University via Coursera)

   • URL: https://www.coursera.org/learn/mlops-mlflow-huggingface-duke

2. MLOps with Databricks (LinkedIn Learning)

   • URL: https://www.linkedin.com/learning/mlops-with-databricks

3. MLOps Tools: MLflow and Hugging Face (LinkedIn Learning)

   • URL: https://www.linkedin.com/learning/mlops-tools-mlflow-and-hugging-face

4. Essentials of MLOps with Azure (LinkedIn Learning)

   • URL: https://www.linkedin.com/learning/essentials-of-mlops-with-azure-2-databricks-mlflow-and-mlflow-tracking

5. Databricks MLOps Training

   • URL: https://www.databricks.com/training/catalog/machine-learning-operations-2403