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This AWS SageMaker course equips you to build, train, and deploy machine learning models on AWS end-to-end. We start by exploring SageMaker's capabilities and accelerators for the ML workflow. Next, you will learn best practices for preparing data, engineering features, and training algorithms efficiently and tuning models for optimal performance, leveraging automation techniques. You will understand hosting trained models on SageMaker endpoints with high scalability, monitoring model accuracy post-deployment, and enabling logs and tracking. Finally, we bring all the concepts together by
Read MoreAmazon SageMaker provides tools for data scientists, ML engineers, business analysts, and developers. The service caters to the unique needs of each role.
No, SageMaker supports multiple languages and frameworks, including Python, R, Java, PyTorch, TensorFlow, MATLAB, and more.
You get 90 days access to the AWS SageMaker course content, including video lectures, notebooks, quizzes, and supplementary resources.
Basic programming knowledge of Python and machine learning concepts is required. Prior AWS and SageMaker experience is useful but not mandatory.
The course begins with foundations and then moves on to advanced topics, making it possible for students with varied ability levels to benefit from the course. Concepts are much simpler to understand when they are explained in detail.
Upon successful completion of the course, you will be awarded with the course completion certificate powered by AWS from SkillUp. This certificate will be delivered to you via email within 6 hours of course completion.