Learn MLOps
28 expert-rated courses covering MLOps. Compared by rating, price, difficulty, and job relevance so you can pick the right one.
The SkillsetCourse catalog emphasizes a comprehensive approach to learning MLOps, offering 29 courses across various platforms including Udemy and DataCamp. With 7 free options and 26 courses awarding certificates, learners can choose based on their needs. Related skills such as model evaluation and machine learning enhance the learning experience and applicability in real-world scenarios.
Catalog analysis updated . Ratings are independent editorial scores. Read the rating methodology.
Key Facts About MLOps
- 1MLOps integrates machine learning and DevOps practices for efficient model management.
- 2SkillsetCourse offers 29 MLOps courses, ensuring diverse learning paths.
- 326 courses provide certificates, enhancing professional credibility.
- 47 free MLOps courses are available, making learning accessible.
- 5Related skills include CI/CD and model deployment, crucial for MLOps success.
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Top MLOps Courses
GenAIOps: Operationalize GenAI Applications
Master GenAIOps practices for deploying and operating generative AI applications in production with Azure AI.
Manage AI-Ready Infrastructure on Azure
Learn to manage GPU clusters, AI workload scheduling, and infrastructure for training and deploying AI models at scale on Azure.
IBM MLOps and AI DevOps Fundamentals
Learn MLOps practices with IBM Cloud Pak and Watson. Cover model lifecycle management, CI/CD for ML, and AI governance.
AI Engineer Professional Certificate Course
Advanced AI engineering course focused on deep learning architectures, transformers, AI agents, and MLOps workflows.
AI Engineer for Data Scientists Associate Certification
Associate certification validating practical AI engineering capabilities for data scientists, including governance and production development.
Machine Learning/AI Engineer
Career path for end-to-end machine learning engineering, including model development, pipelines, and portfolio projects.
Weights & Biases 101
Introductory course on experiment tracking, visualization, collaboration, and optimization workflows in Weights & Biases.
5-Day Gen AI Intensive Course with Google
Self-paced five-day intensive covering LLM foundations, prompting, embeddings, agents, domain LLMs, and GenAI MLOps.
Learn the Foundations of Machine Learning and Artificial Intelligence
11-hour foundational ML/AI course with roadmap guidance, algorithm fundamentals, case studies, and career-oriented workshops.
Advanced Machine Learning on Google Cloud
This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.
Machine Learning Operations (MLOps) on Google Cloud
This learning path is designed for data scientists and ML engineers looking to bridge the gap between machine learning prototypes and production-ready systems on Google Cloud. Learners will explore the full MLOps lifecycle, including feature management with Vertex AI Feature Store, robust model evaluation for predictive and generative AI, and the orchestration of automated workflows. The path concludes with advanced training on building production-grade pipelines using the Kubeflow SDK, Google Cloud components, and AI-driven development with the Data Science Agent.
Professional Machine Learning Engineer Certification
Professional certification for designing, productionizing, scaling, and monitoring AI/ML solutions on Google Cloud, including generative AI task coverage.
Google Cloud AI Infrastructure
This learning path, designed for intermediate to advanced technical learners, offers on-demand courses to specialize in AI Infrastructure on Google Cloud. You'll gain the knowledge and skills to design and deploy high-performance AI/ML solutions using Google Cloud's AI Hypercomputer, GPUs, TPUs, Compute, and Google Kubernetes Engine. Optimize every layer of your AI Infrastructure solutions, from storage and networking to orchestration and best practices, to redefine what's possible with Google AI Infrastructure.
PyTorch for Deep Learning
Professional Certificate by DeepLearning.AI covering PyTorch for deep learning, CNNs, transfer learning, and model deployment.
Hands-On MLOps Fundamentals for ML Engineers
Learn MLOps with hands-on experience using Apache Airflow, Kafka, Spark, and CI/CD pipelines for model deployment.
MLOps | Machine Learning Operations (Duke University)
Learn MLOps from Duke University. Cover model deployment, cloud platforms (AWS, Azure), containerization, and responsible AI.
Microsoft AI & ML Engineering Professional Certificate
Professional certificate covering AI and ML engineering with Microsoft Azure, including model deployment, MLOps, and responsible AI.
Machine Learning on Google Cloud
Build ML models on Google Cloud with TensorFlow, covering feature engineering, model deployment, and MLOps best practices.
Machine Learning Operations (MLOps): Getting Started
Learn MLOps fundamentals from Google Cloud covering model deployment, CI/CD, monitoring, and automation for ML systems.
Edge AI Fundamentals
Learn the fundamentals of deploying AI on edge devices including model optimization, MLOps for edge, and IoT integration.
+ 8 more courses available
Pro Tips for Learning MLOps
- #1Start with foundational courses like 'Learn the Foundations of Machine Learning and Artificial Intelligence' to build essential skills.
- #2Engage with hands-on projects in courses such as 'AI Engineer Professional Certificate Course' to solidify your understanding.
- #3Utilize free resources from platforms like freeCodeCamp to supplement your learning.
- #4Explore related skills like model deployment to enhance your MLOps proficiency.
Why Learn MLOps?
- Learning MLOps can significantly enhance your career prospects in data science and AI.
- MLOps skills are increasingly in demand as organizations adopt machine learning technologies.
- Mastering MLOps prepares you for roles that require both technical and operational expertise.
- Understanding MLOps can lead to improved collaboration between data science and IT teams.