Code: USD75OFF
This is the code repository for Serverless Machine Learning with Amazon Redshift ML, published by Packt.
Create, train, and deploy machine learning models using familiar SQL commands
Amazon Redshift Serverless enables organizations to run petabyte-scale cloud data warehouses quickly and in a cost-effective way, enabling data science professionals to efficiently deploy cloud data warehouses and leverage easy-to-use tools to train models and run predictions. This practical guide will help developers and data professionals working with Amazon Redshift data warehouses to put their SQL knowledge to work for training and deploying machine learning models
This book covers the following exciting features:
- Utilize Redshift Serverless for data ingestion, data analysis, and machine learning
- Create supervised and unsupervised models and learn how to supply your own custom parameters
- Discover how to use time series forecasting in your data warehouse
- Create a SageMaker endpoint and use that to build a Redshift ML model for remote inference
- Find out how to operationalize machine learning in your data warehouse
- Use model explainability and calculate probabilities with Amazon Redshift ML
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
cnt = client.execute_statement(Database='dev',
Sql='Select count(1) from chapter2.orders;',
WorkgroupName=REDSHIFT_WORKGROUP)
query_id = cnt["Id"]
Following is what you need for this book: Data scientists and machine learning developers working with Amazon Redshift who want to explore its machine-learning capabilities will find this definitive guide helpful. A basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to make the most of this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-13).
| Chapter | Software required | OS required |
|---|---|---|
| 1-13 | AWS CLI | Windows, macOS X, and Linux (any) |
Debu Panda, a Senior Manager, Product Management at AWS, is an industry leader in analytics, application platform, and database technologies, and has more than 25 years of experience in the IT world. Debu has published numerous articles on analytics, enterprise Java, and databases and has presented at multiple conferences such as re:Invent, Oracle Open World, and Java One. He is lead author of the EJB 3 in Action (Manning Publications 2007, 2014) and Middleware Management (Packt, 2009).
Phil Bates is a Senior Analytics Specialist Solutions Architect at AWS. He has more than 25 years of experience implementing large scale data warehouse solutions. He is passionate about helping customers through their cloud journey and leveraging the power of ML within their data warehouse.
Bhanu Pittampally is an Analytics Specialist Solutions Architect at Amazon Web Services. His background is in data and analytics and is in the field for over 15 years. He currently lives in Frisco, TX.
Sumeet Joshi is an Analytics Specialist Solutions Architect based out of New York. He specializes in building large-scale data warehousing solutions. He has over 17 years of experience in the data warehousing and analytical space.