Machine learning in the cloud with Azure machine learning

• Verfasst von: Aggarwal, Manuj [VerfasserIn]
• Titel: Machine learning in the cloud with Azure machine learning
• Verf.angabe: Manuj Aggarwal.
• Verlagsort: [Place of publication not identified]
• Verlag: Packt Publishing
• Jahr: 2018
• Umfang: 1 online resource (1 streaming video file (2 hr., 59 min., 37 sec.))
• Illustrationen: digital, sound, color
• Fussnoten: Title from resource description page (Safari, viewed May 22, 2018)
• ISBN: 978-1-78934-752-4
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Windows Azure
• Sach-SW: Cloud computing
• Sach-SW: Machine learning
• Sach-SW: Data mining
• Sach-SW: Electronic videos ; local
• K10plus-PPN: 1680118048
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3607224668
• Lokale URL Inst.: Zum Volltext

Abstract

"With the arrival of cloud computing and multi-core machines, we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data. This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected "past" data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis. You may have experienced various examples of machine learning in your daily life. Machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling. This progress in the field of machine learning is great news for the tech industry and humanity in general. But the downside is that there aren't enough data scientists or machine learning engineers who understand these complex topics. Well, what if there was an easy to use a web service in the cloud, which could do most of the heavy lifting for us? What if it scaled dynamically based on our data volume and velocity? The answer is the new cloud service from Microsoft called Azure Machine Learning."--Resource description page.