Azure Machine Learning Hands-on Labs

In this post I will provide information on Azure Machine Learning (ML) Hands-on Labs training for developers, which we will be delivering in New York and other technology centers. After this training you will know how to create Azure Machine Learning experiment, select best ML model, convert the training experiment to a predictive experiment, and create application which will use the model.

The training consists of following labs.

  1. Predict Individual’s Income >50K (Estimated: 1 hour).
  2. Convert a training experiment into a predictive experiment in Azure ML by Mostafa Elzoghbi (Estimated: 30 minutes).
  3. Consume an Azure ML web service using Visual Studio 2015 by Mostafa Elzoghbi (Estimated: 30 minutes).
  4. Flight delay prediction by Todd Kitta. (Estimated: 3 hours) Start from Task 2. This model can be reused later in a separate Cortana Intelligence Suite End-to-End Training.

If you need more detailed instructions for self-placed training, you may also use Hands-on Labs from edX courses (videos with theory and quizzes are included).

  1. DAT203.1x Data Science Essentials
  2. DAT203.2x Principles of Machine Learning
  3. DAT203.3x Applied Machine Learning

Prerequisites

Please install the below software:

  • Activate your Azure account and bring your Microsoft account credentials. Don’t have a Microsoft account? Sign up now.
  • If you do not have Microsoft Azure account, activate a free 30-day trial Microsoft Azure account, or if you subscribe to MSDN, activate your free Azure MSDN subscriber benefits.
  • Preferred OS is Windows 10.
  • Make sure that Visual Studio 2015 Community, Pro, or Enterprise is installed. Make sure that Office 2013 or later is installed. (Optional; alternatively, you may use Windows Data Science virtual machine in Azure).
  • Create Azure ML workspace for free by signing up here.

Additional resources:

  1. Azure Machine Learning (ML)
  2. Cortana Intelligence Suite: Big Data and Advanced Analytics
  3. Presentation Deck from Azure ML Data Camp (March 2017)

Next Steps:

  1. Cortana Intelligence Suite End-to-End Training (Using the Flight Delay Prediction model in Azure-based solution).
  2. Data Science with Microsoft R Hands-on Labs (Different ways of using R language).

Webcast: Predictive Data Warehouse with Datameer

In the following webcast, we will talk to Andrew Brust, Senior Director of Market Strategy and Intelligence in Datameer.

We will learn about Hadoop ecosystem and PaaS options in Azure, difference of Data Lake and Data Warehouse, and added value of unstructured datastreams. We will discuss Hadoop learning curve for professionals with OLTP database and BI background, and how Datameer can help to create big data solutions and futureproof against the change.

Technologies: HDInsight, Stream Analytics, Azure Data Lake Store and Analytics, Azure Machine Learning and Power BI.

To access the webcast, you will need to fill small registration form.

Webcast: Data warehouse migration to Azure with Hortonworks

Modern EDW should be able to manage both structured and unstructured data to realize full value of data. Security, consistency, and credibility of data is also very important. Data warehouse and big data solutions from Microsoft provide a trusted infrastructure that can handle all types of data, and scale from terabytes to petabytes, with real-time performance.

In this webcast with participation of Mark Lochbihler (Director of Partner Engineering, Hortonworks) we discuss modern enterprise data warehouses (EDW) and migration to Microsoft Cloud (Azure). We will learn about the process, tools, and reference architectures for data warehouse migration.

To access the webcast, you will need to fill small registration form.

Additional resources:

Empowering Insurance Risk Modeling

In today’s global environment volatile financial markets and natural catastrophes have created a fast-moving risk landscape in both life and nonlife insurance. In addition, many insurers must comply with regulatory regimes to show they can cope with the risks they face.

Using Azure’s virtually limitless capacity and unlimited infrastructure resources, Insurance organizations can run their workloads faster and more frequently compared to on-premises. Use of cloud compute allows to achieve larger peaks at higher frequencies with lower TCO and access the compute power needed for even the most complex models (G-Series boxes). Azure meets a broad set of international and compliance standards for risk modeling solutions in Insurance.

risk-in-ms-cloud

In this MTC Studio recording we discuss Insurance Risk Modeling scenarios with Jonathan Silverman, Director of Business Development for Financial Services, Microsoft. We will discuss Azure and hybrid architectures for risk modeling, case studies, partner solutions and regulatory compliance of Microsoft Azure.

To access the webcast, you will need to fill the registration form.

risk-modeling-recording

Additional materials:

Risk Modeling Partner Applications:

Risk Modeling Case Studies: