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.\n\nApply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more\nUse automated machine learning to implement a specific subset of use cases with Amazon SageMaker Autopilot\nDive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, and more\nTie everything together into a repeatable machine learning operations pipeline\nExplore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka\nLearn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
