MLOps (Machine Learning Operations)
Learn the principles and practices for deploying, monitoring, and maintaining machine learning models in production. Bridge the gap between data science and DevOps.
59 courses
Transition from research to production by learning how to package, test, and deploy machine learning models through robust pipelines.
Gain the foundational skills to operationalize and manage machine learning models effectively, from development to deployment, using Azure Machine Learning and MLOps principles.
Learn how to bridge the gap between data science and production by designing continuous integration, delivery, and training pipelines using Azure DevOps and Azure Machine Learning.
Learn to automate, containerize, and monitor machine learning models in production using Docker, Kubernetes, and modern CI/CD workflows.
Learn to transform Transformer models into scalable web applications using modern API frameworks and cloud infrastructure.
Learn to containerize and deploy Python machine learning and NLP models as production-ready APIs using Docker, Flask, and modern MLOps practices.
Learn how to design, train, and deploy production-ready machine learning models using Python, version control, and modern MLOps principles.
Build, deploy, and manage machine learning models on Azure while preparing for the DP-100 certification exam through comprehensive written lessons.
Master the machine learning lifecycle by building automated pipelines, managing deployments with Kubernetes, and monitoring models in production.
Build, train, and deploy predictive machine learning models using a visual drag-and-drop interface without writing any code.
Learn to build, train, and deploy predictive models using cloud-based tools and automated machine learning workflows.
Learn to build, package, and deploy real-world data science, machine learning, and natural language processing applications using Python, Flask, and cloud platforms.
Master the fundamentals of MLOps by building, automating, and scaling end-to-end machine learning workflows using Kubeflow on cloud platforms.
Master the essentials of machine learning operations by versioning data, tracking experiments, and deploying models using MLflow, DVC, Docker, and FastAPI.
Learn to build, evaluate, and deploy predictive machine learning models on the cloud using Azure Machine Learning's low-code tools.
Master the essentials of data analysis and machine learning to extract actionable insights and make informed decisions using modern Python tools.
Master cloud-based machine learning, MLOps, and model deployment to prepare for your professional cloud ML engineer certification.
Build a solid foundation in data science and machine learning, learning how to analyze data and deploy models for real-world industries.
Learn to build, train, and deploy professional machine learning models using TensorFlow within a modern cloud environment.
Master the essentials of designing, training, and deploying Keras machine learning models on Cloud Platform to solve real-world problems.
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