Data Science on AWS

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 up your 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 SageMaker Autopilot
• Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment
• 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

Chapter 1. Introduction to Data Science on AWS Chapter 2. Data Science Use Cases 

Chapter 3. Automated Machine Learning 

Chapter 4. Ingest Data into the Cloud 

Chapter 5. Explore the Dataset 

Chapter 6. Prepare the Dataset for Model Training Chapter 7. Train Your First Model 

Chapter 8. Train and Optimize Models at Scale Chapter 9. Deploy Models to Production 

Chapter 10. Pipelines and MLOps 

Chapter 11. Streaming Analytics and Machine Learning Chapter 12. Secure Data Science on AWS 

About the Authors

Chris Fregly, Principal Developer Advocate, AI and Machine Learning @ AWS (San Francisco)Chris Fregly is a Principal Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is co-author of the O’Reilly Book, “Data Science on AWS.”

Chris is also the Founder of many AI-focused global meetups including the global “Data Science on AWS” Meetup. He regularly speaks at AI and Machine Learning conferences across the world including O’Reilly AI, Open Data Science Conference (ODSC), and Nvidia GPU Technology Conference (GTC).

Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker.

Antje Barth, Senior Developer Advocate, AI and Machine Learning @ AWS (Dusseldorf)

Antje Barth is a Senior Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in Düsseldorf, Germany. She is co-author of the O’Reilly Book, “Data Science on AWS.”

Antje is also co-founder of the Düsseldorf chapter of Women in Big Data. She frequently speaks at AI and Machine Learning conferences and meetups around the world, including the O’Reilly AI and Strata conferences. Besides ML/AI, Antje is passionate about helping developers leverage Big Data, container and Kubernetes platforms in the context of AI and Machine Learning. 

Previously, Antje worked in technical evangelism and solutions engineering at MapR and Cisco where she worked with many companies to build and deploy cloud-based AI solutions using AWS and Kubernetes.