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Getting Started with Amazon SageMaker Studio

Getting Started with Amazon SageMaker Studio

By : Michael Hsieh
4.8 (13)
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Getting Started with Amazon SageMaker Studio

Getting Started with Amazon SageMaker Studio

4.8 (13)
By: Michael Hsieh

Overview of this book

Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment. In this book, you'll start by exploring the features available in Amazon SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals. As you progress, you will learn how these features work together to address common challenges when building ML models in production. After that, you'll understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio. By the end of this book, you'll have learned ML best practices regarding Amazon SageMaker Studio, as well as being able to improve productivity in the ML development life cycle and build and deploy models easily for your ML use cases.
Table of Contents (16 chapters)
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1
Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio
4
Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio
11
Part 3 – The Production and Operation of Machine Learning with SageMaker Studio

SageMaker JumpStart model zoo

There are more than 200 popular prebuilt and pretrained models in SageMaker JumpStart for you to use out of the box or continue to train for your use case. What are they good for? Training an accurate deep learning model is time consuming and complex, even with the most powerful GPU machine. It also requires large amounts of training and labeled data. Now, with these models that have been developed by the community, pretrained on large datasets, you do not have to reinvent the wheel.

Model collection

There are two groups of models: text models and vision models in SageMaker JumpStart model zoo. These models are the most popular ones among the ML community. You can quickly browse the models in SageMaker JumpStart and select the one that meets your needs. On each model page, you will see an introduction to the model, its usage, and how to prepare a dataset for fine-tuning purposes. You can deploy models into AWS as a hosted endpoint for your use case...

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