Data Science on AWS / Fregly, Chris.

Format:

Book

Author/Creator:

Fregly, Chris, author.

Barth, Antje, author.

Language:

English

Subjects (All):

Amazon Web Services (Firm).

Cloud computing.

Machine learning.

Data mining.

Business--Data processing.

Business.

Physical Description:

1 online resource (400 pages)

Edition:

1st edition

Place of Publication:

O'Reilly Media, Inc., 2021.

System Details:

text file

Summary:

If you use data to make critical business decisions, this book is for you. Whether you’re a data analyst, research scientist, data engineer, ML engineer, data scientist, application developer, or systems developer, this guide helps you broaden your understanding of the modern data science stack, create your own machine learning pipelines, and deploy them to applications at production scale. The AWS data science stack unifies data science, data engineering, and application development to help you level up your skills beyond your current role. Authors Antje Barth and Chris Fregly show you how to build your own ML pipelines from existing APIs, submit them to the cloud, and integrate results into your application in minutes instead of days. Innovate quickly and save money with AWS’s on-demand, serverless, and cloud-managed services Implement open source technologies such as Kubeflow, Kubernetes, TensorFlow, and Apache Spark on AWS Build and deploy an end-to-end, continuous ML pipeline with the AWS data science stack Perform advanced analytics on at-rest and streaming data with AWS and Spark Integrate streaming data into your ML pipeline for continuous delivery of ML models using AWS and Apache Kafka

Contents:

Intro

Copyright

Table of Contents

Preface

Overview of the Chapters

Who Should Read This Book

Other Resources

Conventions Used in This Book

Using Code Examples

O'Reilly Online Learning

How to Contact Us

Acknowledgments

Chapter 1. Introduction to Data Science on AWS

Benefits of Cloud Computing

Agility

Cost Savings

Elasticity

Innovate Faster

Deploy Globally in Minutes

Smooth Transition from Prototype to Production

Data Science Pipelines and Workflows

Amazon SageMaker Pipelines

AWS Step Functions Data Science SDK

Kubeflow Pipelines

Managed Workflows for Apache Airflow on AWS

MLflow

TensorFlow Extended

Human-in-the-Loop Workflows

MLOps Best Practices

Operational Excellence

Security

Reliability

Performance Efficiency

Cost Optimization

Amazon AI Services and AutoML with Amazon SageMaker

Amazon AI Services

AutoML with SageMaker Autopilot

Data Ingestion, Exploration, and Preparation in AWS

Data Ingestion and Data Lakes with Amazon S3 and AWS Lake Formation

Data Analysis with Amazon Athena, Amazon Redshift, and Amazon QuickSight

Evaluate Data Quality with AWS Deequ and SageMaker Processing Jobs

Label Training Data with SageMaker Ground Truth

Data Transformation with AWS Glue DataBrew, SageMaker Data Wrangler, and SageMaker Processing Jobs

Model Training and Tuning with Amazon SageMaker

Train Models with SageMaker Training and Experiments

Built-in Algorithms

Bring Your Own Script (Script Mode)

Bring Your Own Container

Pre-Built Solutions and Pre-Trained Models with SageMaker JumpStart

Tune and Validate Models with SageMaker Hyper-Parameter Tuning

Model Deployment with Amazon SageMaker and AWS Lambda Functions

SageMaker Endpoints

SageMaker Batch Transform

Serverless Model Deployment with AWS Lambda.

Streaming Analytics and Machine Learning on AWS

Amazon Kinesis Streaming

Amazon Managed Streaming for Apache Kafka

Streaming Predictions and Anomaly Detection

AWS Infrastructure and Custom-Built Hardware

SageMaker Compute Instance Types

GPUs and Amazon Custom-Built Compute Hardware

GPU-Optimized Networking and Custom-Built Hardware

Storage Options Optimized for Large-Scale Model Training

Reduce Cost with Tags, Budgets, and Alerts

Summary

Chapter 2. Data Science Use Cases

Innovation Across Every Industry

Personalized Product Recommendations

Recommend Products with Amazon Personalize

Generate Recommendations with Amazon SageMaker and TensorFlow

Generate Recommendations with Amazon SageMaker and Apache Spark

Detect Inappropriate Videos with Amazon Rekognition

Demand Forecasting

Predict Energy Consumption with Amazon Forecast

Predict Demand for Amazon EC2 Instances with Amazon Forecast

Identify Fake Accounts with Amazon Fraud Detector

Enable Privacy-Leak Detection with Amazon Macie

Conversational Devices and Voice Assistants

Speech Recognition with Amazon Lex

Text-to-Speech Conversion with Amazon Polly

Speech-to-Text Conversion with Amazon Transcribe

Text Analysis and Natural Language Processing

Translate Languages with Amazon Translate

Classify Customer-Support Messages with Amazon Comprehend

Extract Resume Details with Amazon Textract and Comprehend

Cognitive Search and Natural Language Understanding

Intelligent Customer Support Centers

Industrial AI Services and Predictive Maintenance

Home Automation with AWS IoT and Amazon SageMaker

Extract Medical Information from Healthcare Documents

Self-Optimizing and Intelligent Cloud Infrastructure

Predictive Auto Scaling for Amazon EC2

Anomaly Detection on Streams of Data.

Cognitive and Predictive Business Intelligence

Ask Natural-Language Questions with Amazon QuickSight

Train and Invoke SageMaker Models with Amazon Redshift

Invoke Amazon Comprehend and SageMaker Models from Amazon Aurora SQL Database

Invoke SageMaker Model from Amazon Athena

Run Predictions on Graph Data Using Amazon Neptune

Educating the Next Generation of AI and ML Developers

Build Computer Vision Models with AWS DeepLens

Learn Reinforcement Learning with AWS DeepRacer

Understand GANs with AWS DeepComposer

Program Nature's Operating System with Quantum Computing

Quantum Bits Versus Digital Bits

Quantum Supremacy and the Quantum Computing Eras

Cracking Cryptography

Molecular Simulations and Drug Discovery

Logistics and Financial Optimizations

Quantum Machine Learning and AI

Programming a Quantum Computer with Amazon Braket

AWS Center for Quantum Computing

Increase Performance and Reduce Cost

Automatic Code Reviews with CodeGuru Reviewer

Improve Application Performance with CodeGuru Profiler

Improve Application Availability with DevOps Guru

Chapter 3. Automated Machine Learning

Automated Machine Learning with SageMaker Autopilot

Track Experiments with SageMaker Autopilot

Train and Deploy a Text Classifier with SageMaker Autopilot

Train and Deploy with SageMaker Autopilot UI

Train and Deploy a Model with the SageMaker Autopilot Python SDK

Predict with Amazon Athena and SageMaker Autopilot

Train and Predict with Amazon Redshift ML and SageMaker Autopilot

Automated Machine Learning with Amazon Comprehend

Predict with Amazon Comprehend's Built-in Model

Train and Deploy a Custom Model with the Amazon Comprehend UI

Train and Deploy a Custom Model with the Amazon Comprehend Python SDK

Chapter 4. Ingest Data into the Cloud.

Data Lakes

Import Data into the S3 Data Lake

Describe the Dataset

Query the Amazon S3 Data Lake with Amazon Athena

Access Athena from the AWS Console

Register S3 Data as an Athena Table

Update Athena Tables as New Data Arrives with AWS Glue Crawler

Create a Parquet-Based Table in Athena

Continuously Ingest New Data with AWS Glue Crawler

Build a Lake House with Amazon Redshift Spectrum

Export Amazon Redshift Data to S3 Data Lake as Parquet

Share Data Between Amazon Redshift Clusters

Choose Between Amazon Athena and Amazon Redshift

Reduce Cost and Increase Performance

S3 Intelligent-Tiering

Parquet Partitions and Compression

Amazon Redshift Table Design and Compression

Use Bloom Filters to Improve Query Performance

Materialized Views in Amazon Redshift Spectrum

Chapter 5. Explore the Dataset

Tools for Exploring Data in AWS

Visualize Our Data Lake with SageMaker Studio

Prepare SageMaker Studio to Visualize Our Dataset

Run a Sample Athena Query in SageMaker Studio

Dive Deep into the Dataset with Athena and SageMaker

Query Our Data Warehouse

Run a Sample Amazon Redshift Query from SageMaker Studio

Dive Deep into the Dataset with Amazon Redshift and SageMaker

Create Dashboards with Amazon QuickSight

Detect Data-Quality Issues with Amazon SageMaker and Apache Spark

SageMaker Processing Jobs

Analyze Our Dataset with Deequ and Apache Spark

Detect Bias in Our Dataset

Generate and Visualize Bias Reports with SageMaker Data Wrangler

Detect Bias with a SageMaker Clarify Processing Job

Integrate Bias Detection into Custom Scripts with SageMaker Clarify Open Source

Mitigate Data Bias by Balancing the Data

Detect Different Types of Drift with SageMaker Clarify

Analyze Our Data with AWS Glue DataBrew

Reduce Cost and Increase Performance.

Use a Shared S3 Bucket for Nonsensitive Athena Query Results

Approximate Counts with HyperLogLog

Dynamically Scale a Data Warehouse with AQUA for Amazon Redshift

Improve Dashboard Performance with QuickSight SPICE

Chapter 6. Prepare the Dataset for Model Training

Perform Feature Selection and Engineering

Select Training Features Based on Feature Importance

Balance the Dataset to Improve Model Accuracy

Split the Dataset into Train, Validation, and Test Sets

Transform Raw Text into BERT Embeddings

Convert Features and Labels to Optimized TensorFlow File Format

Scale Feature Engineering with SageMaker Processing Jobs

Transform with scikit-learn and TensorFlow

Transform with Apache Spark and TensorFlow

Share Features Through SageMaker Feature Store

Ingest Features into SageMaker Feature Store

Retrieve Features from SageMaker Feature Store

Ingest and Transform Data with SageMaker Data Wrangler

Track Artifact and Experiment Lineage with Amazon SageMaker

Understand Lineage-Tracking Concepts

Show Lineage of a Feature Engineering Job

Understand the SageMaker Experiments API

Ingest and Transform Data with AWS Glue DataBrew

Chapter 7. Train Your First Model

Understand the SageMaker Infrastructure

Introduction to SageMaker Containers

Increase Availability with Compute and Network Isolation

Deploy a Pre-Trained BERT Model with SageMaker JumpStart

Develop a SageMaker Model

Bring Your Own Script

A Brief History of Natural Language Processing

BERT Transformer Architecture

Training BERT from Scratch

Masked Language Model

Next Sentence Prediction

Fine Tune a Pre-Trained BERT Model

Create the Training Script

Setup the Train, Validation, and Test Dataset Splits.

Set Up the Custom Classifier Model.

Notes:

Online resource; Title from title page (viewed July 25, 2021)

Description based on publisher supplied metadata and other sources.

ISBN:

1-4920-7934-0

1-4920-7936-7

1-4920-7938-3

OCLC:

1246577456