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Developing Big Data Solutions with Azure Machine Learning

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About This Course

The past can often be the key to predicting the future. Big data from historical sources is a valuable resource for identifying trends and building machine learning models that apply statistical patterns and predict future outcomes.

This course introduces Azure Machine Learning, and explores techniques and considerations for using it to build models from big data sources, and to integrate predictive insights into big data processing workflows.

Prerequisites

This course assumes some knowledge of:

  • Building data processing pipelines with Azure Data Factory
  • Building real-time data processing solutions with Azure Stream Analytics

What you'll learn

  • How to create predictive web services with Azure Machine Learning
  • How to work with big data sources in Azure Machine Learning
  • How to integrate Azure Machine Learning into big data batch processing pipelines
  • How to integrate Azure Machine Learning into real-time big data processing solutions

Course Syllabus

Module 1: Introduction to Azure Machine Learning

Module 2: Building Predictive Models with Azure Machine Learning

Module 3: Operationalizing Machine Learning Models

Module 4: Using Azure Machine Learning in Big Data Solutions

Meet the instructors

Course Staff Image #1

Graeme Malcolm

Senior Content Developer

Microsoft Learning Experiences

Graeme has been a trainer, consultant, and author for longer than he cares to remember, specializing in SQL Server and the Microsoft data platform. He is a Microsoft Certified Solutions Expert for the SQL Server Data Platform and Business Intelligence. After years of working with Microsoft as a partner and vendor, he now works in the Microsoft Learning Experiences team as a senior content developer, where he plans and creates content for developers and data professionals who want to get the best out of Microsoft technologies.

Course Staff Image #2

Dr. Steve Elston

Managing Director

Quantia Analytics, LLC

Steve is a big data geek and data scientist, with over two decades of experience using R and S/SPLUS for predictive analytics and machine learning. He holds a PhD degree in Geophysics from Princeton University, and has led multi-national data science teams across various companies

  1. Código del curso

    DAT228x
  2. Inicio de clases

  3. Término de clases

  4. Esfuerzo estimado

    12-16 hours in total
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