Data Science on AWS. Implementing End-to-End, Continuous AI and Machine Learning Pipelines 21538

 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

Overview of the Chapters

Chapter 1 provides an overview of the broad and deep Amazon AI and ML stack, an enormously powerful and diverse set of services, open source libraries, and infrastructure to use for data science projects of any complexity and scale.

Chapter 2 describes how to apply the Amazon AI and ML stack to real-world use cases for recommendations, computer vision, fraud detection, natural language understanding (NLU), conversational devices, cognitive search, customer support, industrial predictive maintenance, home automation, Internet of Things (IoT), healthcare, and quantum computing.

Chapter 3 demonstrates how to use AutoML to implement a specific subset of these use cases with SageMaker Autopilot.

Chapters 4–9 dive deep into the complete model development life cycle (MDLC) for a BERT-based NLP use case, including data ingestion and analysis, feature selection and engineering, model training and tuning, and model deployment with Amazon SageMaker, Amazon Athena, Amazon Redshift, Amazon EMR, TensorFlow, PyTorch, and serverless Apache Spark.

Chapter 10 ties everything together into repeatable pipelines using MLOps with SageMaker Pipelines, Kubeflow Pipelines, Apache Airflow, MLflow, and TFX.

Chapter 11 demonstrates real-time ML, anomaly detection, and streaming analytics on real-time data streams with Amazon Kinesis and Apache Kafka.

Chapter 12 presents a comprehensive set of security best practices for data science projects and workflows, including IAM, authentication, authorization, network isolation, data encryption at rest, post-quantum network encryption in transit, governance, and auditability.

Throughout the book, we provide tips to reduce cost and improve performance for data science projects on AWS.