Learn MLOps
23 expert-rated courses covering MLOps. Compared by rating, price, difficulty, and job relevance so you can pick the right one.
MLOps skills are in high demand across industries like tech, finance, healthcare, and manufacturing, where advanced analytics and AI-powered automation are critical. MLOps engineers can command a 25-40% salary premium over traditional software engineers. The MLOps job market is expected to grow 35% annually through 2026.
Key Facts About MLOps
- 1MLOps bridges the gap between ML model development and production deployment, enabling faster, more reliable AI application rollouts.
- 2Key MLOps disciplines include model versioning, continuous integration/deployment, monitoring and observability, and automated retraining.
- 3Leading MLOps tools include MLflow, Kubeflow, TensorFlow Extended (TFX), and Amazon SageMaker.
- 4MLOps leverages DevOps principles like Infrastructure as Code, GitOps, and Chaos Engineering to bring DevOps rigor to the ML lifecycle.
- 5Successful MLOps implementation can reduce ML model deployment time by 40-70% and improve production model performance by 15-30%.
Available on
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.
Professional Machine Learning Engineer Certification
Professional certification for designing, productionizing, scaling, and monitoring AI/ML solutions on Google Cloud, including generative AI task coverage.
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.
AI Engineer Professional
Advanced specialization covering MLOps, CNNs, RNNs, generative AI agents, LangGraph, Keras, and production-ready AI systems.
AI Agents and MLOps for Production-Ready AI
Learn AI agents and MLOps for production including LangGraph, CrewAI, Docker, Kubernetes, and cloud deployment on AWS/GCP/Azure.
Deploy ML Models to Production
Learn to deploy ML models to production with AWS SageMaker covering model governance, data security, and GDPR compliance.
+ 3 more courses available
Pro Tips for Learning MLOps
- #1Start by mastering the fundamentals of software engineering, cloud infrastructure, and containerization.
- #2Gain hands-on experience with MLOps tools and frameworks through online courses and personal projects.
- #3Stay up-to-date with the latest MLOps best practices and industry trends by following thought leaders and technical blogs.
Why Learn MLOps?
- Become the bridge between ML research and real-world business impact, making you a highly valuable asset.
- Gain the full-stack skills to take ML models from prototype to scalable, production-ready systems.
- Future-proof your career in the rapidly evolving AI industry, where MLOps skills are in acute shortage.