This is the code repository for Applied Machine Learning and High-Performance Computing on AWS, published by Packt.

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. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.

This book covers the following exciting features:

• Explore data management, storage, and fast networking for HPC applications
• Focus on the analysis and visualization of a large volume of data using Spark
• Train visual transformer models using SageMaker distributed training
• Deploy and manage ML models at scale on the cloud and at the edge
• Get to grips with performance optimization of ML models for low latency workloads
• Apply HPC to industry domains such as CFD, genomics, AV, and optimization

If you feel this book is for you, get your copy today!

All of the code is organized into folders.

The code will look like the following:

import numpy as np
import json

with open("horse_cart.jpg", "rb") as f:
   payload = f.read()
   payload = bytearray(payload)

Following is what you need for this book: The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. 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. 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.

With the following software and hardware list you can run all code files present in the book (Chapter 1-14).

To follow the text and examples in this book, you are recommended to have a foundational knowledge of Python and HPC, and an intermediate understanding of data analysis, ML, and AI. In addition, you should have access to the following technology tools to work through the code and experimentation examples:

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Accelerate Deep Learning Workloads with Amazon SageMaker [Packt] [Amazon]

• Machine Learning Engineering on AWS [Packt] [Amazon]

Mani Khanuja is a seasoned IT professional with over 17 years of software engineering experience. She has successfully led machine learning and artificial intelligence projects in various domains, such as forecasting, computer vision, and natural language processing. At AWS, she helps customers to build, train, and deploy large machine learning models at scale. She also specializes in data preparation, distributed model training, performance optimization, machine learning at the edge, and automating the complete machine learning life cycle to build repeatable and scalable applications.

Farooq Sabir is a research and development expert in machine learning, data science, big data, predictive analytics, computer vision, and image and video processing. He has over 10 years of professional experience.

Shreyas Subramanian helps AWS customers build and fine-tune large-scale machine learning and deep learning models, and rearchitect solutions to help improve the security, scalability, and efficiency of machine learning platforms. He also specializes in setting up massively parallel distributed training, hyperparameter optimization, and reinforcement learning solutions, and provides reusable architecture templates to solve AI and optimization use cases.

Trenton Potgieter is an expert technologist with 25 years of both local and international experience across multiple aspects of an organization; from IT to sales, engineering, and consulting, on the cloud and on-premises. He has a proven ability to analyze, assess, recommend, and design appropriate solutions that meet key business criteria, as well as present and teach them from engineering to executive levels.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

https://packt.link/free-ebook/9781803237015