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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

Exploring data with visualization

Exploratory data analysis (EDA) provides insights into the data at hand and helps us strategize the data transformation so that ML modeling can be the most performant. Analyzing and visualizing data with programming is robust and scalable but it requires lots of coding and development. Using SageMaker Data Wrangler, you can easily create charts and figures in the UI. Currently, SageMaker Data Wrangler supports the following types of chart and analysis that do not require coding: histogram, scatter plot, bias report, multicollinearity, quick model, target leakage, and table summary. Let's take a look at how they work one by one.

Understanding the frequency distribution with a histogram

The histogram helps us understand the frequency distribution of a variable whose values are bucketed into discrete intervals with a bar graph. We can use the histogram function in SageMaker Data Wrangler to see, for example, how long callers spend making calls...

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