3 days / 40+ speakers
12 workshops

May 17-19, 2017 | Vilnius, Lithuania
Yonder, Romania

Silviu Niculita

Silviu Niculita is a passionate software developer and technologist focused on solving hard business problems with cutting-edge technology. For the last 8 years he has managed teams building and operating high-performance enterprise systems from hardware architecture to design, development, quality assurance and support.

Silviu’s areas of interest are around how small teams can be most effective in building real software: high-quality, secure systems at the highest limits of robustness, performance and flexibility. Silviu has worked with groups like Microsoft and Siemens and he also holds numerous IT certifications.”


Cloud Powered Big Data for Mere Mortals with Azure SQL Data Warehouse

Finally, you can use elastic, relational, and data warehouse in the same sentence. Azure SQL Data Warehouse is a scale out database service designed to answer your ad hoc queries across petabyte scale data-sets through massively parallel processing. See how you can optimize costs by independently scaling compute and storage resources in seconds.





Machine learning for mere mortals with Azure ML

Machine learning has been leveraged to radically change many industry verticals. The problem is the learning curve has always been very steep. Exotic languages, complex tools, little or no documentation.

But innovative cloud based ML platforms are changing that and democratizing access. During this session, you will learn the basics of machine learning, and you will see a demo of how you can build a prediction model using real-world data, evaluate several different algorithms and modeling strategies, then deploy the finished model as a scalable RESTful API within minutes.

Workshop Format: Half-Day hands-on workshop using your own laptop.

Workshop Agenda

  • 01 | Introduction to Machine Learning & Azure ML Studio.
    Learn what Machine Learning is and its benefits, see some of the industry verticals that that are heavily leveraging it, hear some very innovative and creative uses of ML and get a quick introduction to basic principles and workflows. See a demo of the Azure Portal, provisioning a workspace and granting users access to it.
  • 02 | Designing a Predictive API with Azure ML
    Perform several end-to-end demos and recreate recommendation models (both supervised and unsupervised) from scratch in ML Studio using real-world sample data-sets.
  • 03 | Productize and Monetize your ML API
    Perform a demo of publishing and scaling up & down the finished API.
    Then, walk through the process of monetizing your recommendation models through the Azure Data Marketplace.
  • 04 | Azure ML APIs Extensibility Scenarios and Cortana Intelligence Suite Overview
    Learn how to best leverage the automatically generated C# code in the web service API, and run that code in Visual Studio. This code calls the API from the web service and returns the results, which can be used to embed Machine Learning technologies in 3rd party systems.Get a 10.000 foot understanding of the wider tool-set that Microsoft Azure has to offer in the space of advanced analytics. Discuss which data acquisition, data storage, data analysis and data presentation option is best for your use case.

Participants Will Learn

  • Key concepts of data science and machine learning;
  • Data cleansing and splitting for predictive modeling;
  • Data visualization and exploration;
  • Creation of supervised and unsupervised machine learning models;
  • Deployment and monetization of machine learning web services;
  • Basic understanding of Microsoft’s offering in the advanced analytics space.

About Azure ML
Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data.

Machine Learning Studio publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.

Machine Learning Studio is where data science, predictive analytics, cloud resources, and your data meet.

1. An active Azure subscription. A trial will suffice.
2. Some experience in data warehousing and analytics is preferable but not necessary.