Apress - Agile Machine Learning: Effective Machine Learning Inspired By The Agile Manifesto (2019 EN)

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    Author: Eric Carter, Matthew Hurst
    Full Title: Agile Machine Learning: Effective Machine Learning Inspired By The Agile Manifesto
    Publisher: Apress; 1st ed. edition (August 22, 2019)
    Year: 2019
    ISBN-13: 9781484251072 (978-1-4842-5107-2), 9781484251065 (978-1-4842-5106-5)
    ISBN-10: 1484251075, 1484251067
    Pages: 248
    Language: English
    Genre: Educational: Microsoft and .NET
    File type: EPUB (True), PDF (True)
    Quality: 10/10
    Price: 40.65 €


    Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.

    Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment.

    The authors’ approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product.


    Learn:
    ✓ Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused
    ✓ Make sound implementation and model exploration decisions based on the data and the metrics
    ✓ Know the importance of data wallowing: analyzing data in real time in a group setting
    ✓ Recognize the value of always being able to measure your current state objectively
    ✓ Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations

    Features:
    ✓ Authors have proven real-world experience with numerous big data projects coordinated across distributed teams for multiple Microsoft markets
    ✓ Teaches you how to manage projects involving machine learning more effectively in a production environment
    ✓ Shows you, by example, how to deliver superior data products through agile processes and organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment

    Who This Book Is For:
    Anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.

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