MLOps Engineering on AWS: A Simple Guide
MLOps Engineering on AWS is an AWS training course that focuses on managing the complete lifecycle of machine learning models, combining ML, DevOps, and automation using AWS services.
Why it matters
MLOps Engineering on AWS helps teams build secure and reliable machine learning systems while deploying models faster, making it a valuable solution for developers and DevOps engineers working with ML on AWS.
Key Points
- 1Automates the process of building, training, testing, deploying, and monitoring machine learning models
- 2Supports version control for data and models, automated pipelines, CI/CD, and monitoring model performance
- 3Applies DevOps principles like automation, continuous integration, and continuous deployment to machine learning workflows
- 4Commonly uses Python, Java, Scala, and R programming languages
Details
MLOps Engineering on AWS is a comprehensive approach to managing the lifecycle of machine learning models on the AWS platform. It combines best practices from Machine Learning (ML), DevOps, and automation to help teams build reliable, scalable, and production-ready ML systems. The course covers the entire ML workflow, including data versioning, automated model training and testing, continuous deployment, and real-time monitoring. By applying DevOps principles to ML, it reduces manual work, improves consistency, and enables faster deployment of models. The solution supports multiple programming languages like Python, Java, Scala, and R, depending on the AWS services and frameworks used. MLOps Engineering on AWS is a paid course offered by Amazon Web Services, the parent company, and is not open-source. However, the underlying AWS tools and technologies often support open-source technologies.
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