It is not an easy task to design and build systems in the cloud that involve Machine Learning and Data Science requirements. It also requires careful planning and execution to get different teams and professionals such as data scientists and members of MLOps teams to follow certain processes in order to have a sustainable and effective ML workflow. In this talk, I will share the different strategies and solutions on how to design, build, deploy, and maintain complex intelligent systems in AWS using Amazon SageMaker. Amazon SageMaker is a fully managed machine learning service that aims to help developers, data scientists, machine learning practitioners, and MLOps teams manage machine learning experiments and workflows.
We will start by discussing some of the important concepts and patterns used in production environments and systems. As we discuss these concepts and patterns, we will provide a couple of practical solutions and examples on using the different features and capabilities of Amazon SageMaker to solve the different needs of data science and MLOps teams.
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