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    Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition Rich Collier, Camilla Montonen, Bahaaldine Azarmi

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition Rich Collier, Camilla Montonen, Bahaaldine Azarmi - okladka książki

    Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition Rich Collier, Camilla Montonen, Bahaaldine Azarmi - okladka książki

    Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition Rich Collier, Camilla Montonen, Bahaaldine Azarmi - audiobook MP3

    Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition Rich Collier, Camilla Montonen, Bahaaldine Azarmi - audiobook CD

    Autorzy:
    Rich Collier, Camilla Montonen, Bahaaldine Azarmi
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    450
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection.
    The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with.
    By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.

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    O autorach książki

    Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts.
    Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
    Bahaaldine Azarmi, or Baha for short, is the head of solutions architecture in the EMEA South region at Elastic. Prior to this position, Baha co-founded ReachFive, a marketing data platform focused on user behavior and social analytics. He has also worked for a number of different software vendors, including Talend and Oracle, where he held positions as a solutions architect and architect. Prior to Machine Learning with the Elastic Stack, Baha authored books including Learning Kibana 5.0, Scalable Big Data Architecture, and Talend for Big Data. He is based in Paris and holds an MSc in computer science from Polytech'Paris.

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