Applied Machine Learning and High-Performance Computing on AWS : Accelerate the Development of Machine Learning Applications Following Architectural Best Practices

Cover -- Title Page -- Copyright &amp -- Credit -- Contributors -- Table of Contents -- Preface -- Part 1: Introducing High-Performance Computing -- Chapter 1: High-Performance Computing Fundamentals -- Why do we need HPC? -- Limitations of on-premises HPC -- Barrier to innovation -- Reduced efficiency -- Lost opportunities -- Limited scalability and elasticity -- Benefits of doing HPC on the cloud -- Drives innovation -- Enables secure collaboration among distributed teams -- Amplifies operational efficiency -- Optimizes performance -- Optimizes cost -- Driving innovation across industries with HPC -- Life sciences and healthcare -- AVs -- Supply chain optimization -- Summary -- Further reading -- Chapter 2: Data Management and Transfer -- Importance of data management -- Challenges of moving data into the cloud -- How to securely transfer large amounts of data into the cloud -- AWS online data transfer services -- AWS DataSync -- AWS Transfer Family -- Amazon S3 Transfer Acceleration -- Amazon Kinesis -- AWS Snowcone -- AWS offline data transfer services -- Process for ordering a device from AWS Snow Family -- Summary -- Further reading -- Chapter 3: Compute and Networking -- Introducing the AWS compute ecosystem -- General purpose instances -- Compute optimized instances -- Accelerated compute instances -- Memory optimized instances -- Storage optimized instances -- Amazon Machine Images (AMIs) -- Containers on AWS -- Serverless compute on AWS -- Networking on AWS -- CIDR blocks and routing -- Networking for HPC workloads -- Selecting the right compute for HPC workloads -- Pattern 1 - a standalone instance -- Pattern 2 - using AWS ParallelCluster -- Pattern 3 - using AWS Batch -- Pattern 4 - hybrid architecture -- Pattern 5 - Container-based distributed processing -- Pattern 6 - serverless architecture -- Best practices for HPC workloads ; Summary -- References -- Chapter 4: Data Storage -- Technical requirements -- AWS services for storing data -- Amazon Simple Storage Service (S3) -- Amazon Elastic File System (EFS) -- Amazon EBS -- Amazon FSx -- Data security and governance -- IAM -- Data protection -- Data encryption -- Logging and monitoring -- Resilience -- Tiered storage for cost optimization -- Amazon S3 storage classes -- Amazon EFS storage classes -- Choosing the right storage option for HPC workloads -- Summary -- Further reading -- Part 2: Applied Modeling -- Chapter 5: Data Analysis -- Technical requirements -- Exploring data analysis methods -- Gathering the data -- Understanding the data structure -- Describing the data -- Visualizing the data -- Reviewing the data analytics life cycle -- Reviewing the AWS services for data analysis -- Unifying the data into a common store -- Creating a data structure for analysis -- Visualizing the data at scale -- Choosing the right AWS service -- Analyzing large amounts of structured and unstructured data -- Setting up EMR and SageMaker Studio -- Analyzing large amounts of structured data -- Analyzing large amounts of unstructured data -- Processing data at scale on AWS -- Cleaning up -- Summary -- Chapter 6: Distributed Training of Machine Learning Models -- Technical requirements -- Building ML systems using AWS -- Introducing the fundamentals of distributed training -- Reviewing the SageMaker distributed data parallel strategy -- Reviewing the SageMaker model data parallel strategy -- Reviewing a hybrid data parallel and model parallel strategy -- Executing a distributed training workload on AWS -- Executing distributed data parallel training on Amazon SageMaker -- Executing distributed model parallel training on Amazon SageMaker -- Summary -- Chapter 7: Deploying Machine Learning Models at Scale -- Managed deployment on AWS ; Amazon SageMaker managed model deployment options -- The variety of compute resources available -- Cost-effective model deployment -- Blue/green deployments -- Inference recommender -- MLOps integration -- Model registry -- Elastic inference -- Deployment on edge devices -- Choosing the right deployment option -- Using batch inference -- Using real-time endpoints -- Using asynchronous inference -- Batch inference -- Creating a transformer object -- Creating a batch transform job for carrying out inference -- Optimizing a batch transform job -- Real-time inference -- Hosting a machine learning model as a real-time endpoint -- Asynchronous inference -- The high availability of model endpoints -- Deployment on multiple instances -- Endpoints autoscaling -- Endpoint modification without disruption -- Blue/green deployments -- All at once -- Canary -- Linear -- Summary -- References -- Chapter 8: Optimizing and Managing Machine Learning Models for Edge Deployment -- Technical requirements -- Understanding edge computing -- Reviewing the key considerations for optimal edge deployments -- Efficiency -- Performance -- Reliability -- Security -- Designing an architecture for optimal edge deployments -- Building the edge components -- Building the ML model -- Deploying the model package -- Summary -- Chapter 9: Performance Optimization for Real-Time Inference -- Technical requirements -- Reducing the memory footprint of DL models -- Pruning -- Quantization -- Model compilation -- Key metrics for optimizing models -- Choosing the instance type, load testing, and performance tuning for models -- Observing the results -- Summary -- Chapter 10: Data Visualization -- Data visualization using Amazon SageMaker Data Wrangler -- SageMaker Data Wrangler visualization options -- Adding visualizations to the data flow in SageMaker Data Wrangler -- Data flow ; Amazon's graphics-optimized instances -- Benefits and key features of Amazon's graphics-optimized instances -- Summary -- Further reading -- Part 3: Driving Innovation Across Industries -- Chapter 11: Computational Fluid Dynamics -- Technical requirements -- Introducing CFD -- Reviewing best practices for running CFD on AWS -- Using AWS ParallelCluster -- Using CFD Direct -- Discussing how ML can be applied to CFD -- Summary -- References -- Chapter 12: Genomics -- Technical requirements -- Managing large genomics data on AWS -- Designing architecture for genomics -- Applying ML to genomics -- Protein secondary structure prediction for protein sequences -- Summary -- Chapter 13: Autonomous Vehicles -- Technical requirements -- Introducing AV systems -- AWS services supporting AV systems -- Designing an architecture for AV systems -- ML applied to AV systems -- Model development -- Step 1 - build and push the CARLA container to Amazon ECR -- Step 2 - configure and run CARLA on RoboMaker -- Summary -- References -- Chapter 14: Numerical Optimization -- Introduction to optimization -- Goal or objective function -- Variables -- Constraints -- Modeling an optimization problem -- Optimization algorithm -- Local and global optima -- Common numerical optimization algorithms -- Random restart hill climbing -- Simulated annealing -- Tabu search -- Evolutionary methods -- Example use cases of large-scale numerical optimization problems -- Traveling salesperson optimization problem -- Worker dispatch optimization -- Assembly line optimization -- Numerical optimization using high-performance compute on AWS -- Commercial optimization solvers -- Open source optimization solvers -- Numerical optimization patterns on AWS -- Machine learning and numerical optimization -- Summary -- Further reading -- Index -- Other Books You May Enjoy