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    Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter - okladka książki

    Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter - okladka książki

    Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter - audiobook MP3

    Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter - audiobook CD

    Autorzy:
    Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    382
    Dostępne formaty:
         PDF
         ePub
    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 provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. 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.

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    O autorach książki

    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.

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