Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices
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Cover Page....2 Table of Contents....3 Preface....5 Part 1: Introducing High-Performance Computing....14 Chapter 1: High-Performance Computing Fundamentals....15 Why do we need HPC?....16 Limitations of on-premises HPC....17 Benefits of doing HPC on the cloud....19 Driving innovation across industries with HPC....22 Summary....26 Further reading....27 Chapter 2: Data Management and Transfer....28 Importance of data management....28 Challenges of moving data into the cloud....30 How to securely transfer large amounts of data into the cloud....32 AWS online data transfer services....34 AWS offline data transfer services....47 Summary....58 Further reading....58 Chapter 3: Compute and Networking....60 Introducing the AWS compute ecosystem....61 Networking on AWS....74 Selecting the right compute for HPC workloads....81 Best practices for HPC workloads....89 Summary....94 References....94 Chapter 4: Data Storage....98 Technical requirements....99 AWS services for storing data....99 Data security and governance....125 Tiered storage for cost optimization....128 Choosing the right storage option for HPC workloads....131 Summary....133 Further reading....133 Part 2: Applied Modeling....135 Chapter 5: Data Analysis....136 Technical requirements....137 Exploring data analysis methods....137 Reviewing the AWS services for data analysis....142 Analyzing large amounts of structured and unstructured data....146 Processing data at scale on AWS....172 Cleaning up....173 Summary....174 Chapter 6: Distributed Training of Machine Learning Models....175 Technical requirements....176 Building ML systems using AWS....176 Introducing the fundamentals of distributed training....180 Executing a distributed training workload on AWS....190 Summary....205 Chapter 7: Deploying Machine Learning Models at Scale....207 Managed deployment on AWS....208 Choosing the right deployment option....213 Batch inference....217 Real-time inference....225 Asynchronous inference....233 The high availability of model endpoints....235 Blue/green deployments....240 Summary....242 References....242 Chapter 8: Optimizing and Managing Machine Learning Models for Edge Deployment....244 Technical requirements....245 Understanding edge computing....245 Reviewing the key considerations for optimal edge deployments....246 Designing an architecture for optimal edge deployments....250 Summary....271 Chapter 9: Performance Optimization for Real-Time Inference....272 Technical requirements....273 Reducing the memory footprint of DL models....273 Key metrics for optimizing models....287 Choosing the instance type, load testing, and performance tuning for models....289 Observing the results....297 Summary....299 Chapter 10: Data Visualization....300 Data visualization using Amazon SageMaker Data Wrangler....301 Amazon’s graphics-optimized instances....318 Summary....319 Further reading....319 Part 3: Driving Innovation Across Industries....321 Chapter 11: Computational Fluid Dynamics....322 Technical requirements....322 Introducing CFD....323 Reviewing best practices for running CFD on AWS....329 Discussing how ML can be applied to CFD....355 Summary....358 References....358 Chapter 12: Genomics....360 Technical requirements....361 Managing large genomics data on AWS....361 Designing architecture for genomics....363 Applying ML to genomics....364 Summary....385 Chapter 13: Autonomous Vehicles....386 Technical requirements....387 Introducing AV systems....387 AWS services supporting AV systems....391 Designing an architecture for AV systems....394 ML applied to AV systems....399 Summary....424 References....425 Chapter 14: Numerical Optimization....429 Introduction to optimization....430 Common numerical optimization algorithms....440 Example use cases of large-scale numerical optimization problems....446 Numerical optimization using high-performance compute on AWS....458 Machine learning and numerical optimization....463 Summary....465 Further reading....466 Index....468 Why subscribe?....503 Other Books You May Enjoy....504 Packt is searching for authors like you....508 Share Your Thoughts....508 Download a free PDF copy of this book....509
Описание
В этом материале разберём тему: models.
Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.
It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases.
By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.
This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. What You Will Learn:Explore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is for:The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.
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автор — Khanuja Mani , Potgieter Trenton , Sabir Farooq , Subramanian Shreyas, издательство Packt Publishing Limited, год выпуска 2022, 510 страниц.
О чём книга «Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices»?
Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications.
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