AWS Certified Machine Learning: Advanced SageMaker Functionality

Amazon Web Services    |    Intermediate
  • 13 videos | 1h 23m 46s
  • Includes Assessment
  • Earns a Badge
Rating 4.7 of 9 users Rating 4.7 of 9 users (9)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 2m 16s
  • 5m 55s
    In this video, you will list the frameworks that are supported in Amazon SageMaker for native code. FREE ACCESS
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    3.  Training Keras/Tensorflow model in SageMaker
    6m 4s
    After completing this video, you will be able to work with training Keras/Tensorflow models on SageMaker. FREE ACCESS
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    4.  Connecting Amazon EMR with SageMaker Notebooks
    5m 20s
    In this video, find out how to use the integrated capabilities in SageMaker to connect EMR clusters with SageMaker Notebooks. FREE ACCESS
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    5.  Using SageMaker for Incremental Spot Training
    8m 10s
    Upon completion of this video, you will be able to work with SageMaker to tune models over time and manage training and tuning costs by using Spot training. FREE ACCESS
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    6.  Distributed Training in SageMaker
    9m 6s
    During this video, you will learn how to describe the distributed capabilities of SageMaker and its different methods. FREE ACCESS
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    7.  Tackling Distributed Training with PyTorch
    5m 5s
    Find out how to work with distributed data and model parallel training practices to improve your Pytorch model. FREE ACCESS
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    8.  Working with SageMaker Autopilot
    11m 44s
    Learn how to work with SageMaker Autopilot to automate the key stages in a machine learning project, such as data exploration, model training, and tuning. FREE ACCESS
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    9.  Working with SageMaker Debugger
    6m 4s
    In this video, you will learn how to work with SageMaker Debugger to debug, monitor, and profile training jobs in real-time and reduce the costs of your machine learning models by optimizing resources. FREE ACCESS
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    10.  Working with SageMaker Experiments
    6m 16s
    During this video, you will discover how to work with SageMaker Experiments to organize, track, compare, and evaluate iterative machine learning experiments. FREE ACCESS
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    11.  Building ML Explainability in SageMaker
    6m 36s
    In this video, you will learn how to work with SageMaker Clarify to build explainable machine learning models. FREE ACCESS
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    12.  Performing Bias Detection in SageMaker
    9m 48s
    Discover how to work with SageMaker Clarify to analyze the post-training bias of machine learning models. FREE ACCESS
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    13.  Course Summary
    1m 23s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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