Certification     Amazon     AWS Certified Machine Learning – Specialty     AWS Certified Machine Learning – Specialty

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Amazon SageMaker can be used with multiple other frameworks and toolkits to precisely define machine learning (ML) algorithms and train models and is not limited to a specific set of algorithms for ML. SageMaker also provides a wide range of tools that can be used for incremental training, distributed training, debugging, or explainability.
Use this course to learn about advanced SageMaker functionality, including supported frameworks, Amazon EMR, and autoML. You'll also gain hands-on experience in using new features, such as SageMaker Experiments, SageMaker Debugger, Bias Detection, and Explainability.
Once you have finished this course, you'll have the skills and knowledge to implement SageMaker's advanced functionalities. Further, you'll be a step closer to preparing for the AWS Certified Machine Learning – Specialty certification exam.

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Objectives

AWS Certified Machine Learning: Advanced SageMaker Functionality discover the key concepts covered in this course list the frameworks that are supported in Amazon SageMaker for native code work with training Keras/Tensorflow models with SageMaker use the integrated capabilities in SageMaker to connect EMR clusters with SageMaker Notebooks work with SageMaker to tune models over time and manage training and tuning costs by using Spot training describe the distributed capabilities of SageMaker and its different methods work with distributed data and model parallel training practices to your Pytorch model work with SageMaker Autopilot to automate the key stages in a machine learning project, such as data exploration, model training, and tuning work with SageMaker Debugger to debug, monitor, and profile training jobs in real-time and reduce costs of your machine learning models by optimizing resources work with SageMaker Experiments to organize, track, compare, and evaluate iterative machine learning experiments work with SageMaker Clarify to build explainable machine learning models work with SageMaker Clarify to analyze post-training bias of machine learning models summarize the key concepts covered in this course