Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines
- Length: 522 pages
- Edition: 1
- Language: English
- Publisher: O'Reilly Media
- Publication Date: 2021-05-04
- ISBN-10: 1492079391
- ISBN-13: 9781492079392
- Sales Rank: #51112 (See Top 100 Books)
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.
- Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more
- Use automated machine learning to implement a specific subset of use cases with Amazon SageMaker Autopilot
- Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, and more
- Tie everything together into a repeatable machine learning operations pipeline
- Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka
- Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Table of contents
1. Introduction to Data Science on AWS
2. Data Science Use Cases
3. Automated Machine Learning
4. Ingest Data into the Cloud
5. Explore the Dataset
6. Prepare the Dataset for Model Training
7. Train Your First Model
8. Train and Optimize Models at Scale
9. Deploy Models to Production
10. Pipelines and MLOps
11. Streaming Analytics and Machine Learning
12. Secure Data Science on AWS
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