Big Data and Advanced Analytics: On-demand and Upcoming Live Webinars


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Building a Scalable Data Science Platform with R and Hadoop
Presented March 29, 2016

Hadoop is famously scalable. Cloud Computing is famously scalable. R – the thriving and extensible open source Data Science software – not so much. But what if we seamlessly combined Hadoop, Cloud Computing, and R to create a scalable Data Science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based Web Service. Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms at scale.

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Hadoop Without Borders – Learn to Build Hybrid Big Data Analytics Pipelines with Azure Data Factory
Presented April 12, 2016

Do you have sensitive data that you need to anonymize and aggregate before sending it to the cloud? Or perhaps you want to extend your on-premises Hadoop data lake with new workflows to leverage the benefits of the cloud’s elastic scale? If so, chances are you need a hybrid data integration solution. In this talk we will cover how to build hybrid data flows that span on-premises and cloud-based Hadoop using Azure Data Factory.

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Data Science for Data Professionals
Presented April 26, 2016

As a data professional, do you wonder how you can leverage data science for creating new value in your organization?

This webinar is for you. Tune in to this webinar to learn how you can leverage your familiar knowledge on working with databases, and learn how you can get started with doing data science with databases.

Using a concrete example of building a predictive customer churn model for mobile service provider, we’ll share how you can jumpstart. You will learn:

  • How you can leverage data science to turn your database applications into intelligent applications
  • How you can leverage the exciting innovations in the database with the best of R
  • How you can use your existing Business Intelligence (BI) tools to harness these new capabilities

We will unveil how you can build powerful predictive customer churn models using SQL Server 2016, and deliver the insights using Business Intelligence (BI) tools.

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How to Monitor & Manage Big Data Pipelines with Azure Data Factory
Presented March 15, 2016

Big data pipelines can span many cloud and on-premises storage and compute resources and have many complex scheduling dependencies. It can be hard to monitor and troubleshoot all of the different activities you may have running with Azure Data Lake, Azure SQL DW, HDInsight and other services. In this webinar, see the brand new Azure Data Factory application. Learn how to deploy, monitor and manage complex data pipelines with one simple interface and consistently deliver refined data to feed your BI and other analytics tools.

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Azure Data Lake Store - The Scalable & Secure Big Data Store
Tuesday, March 1, 2016 

Azure Data Lake Store is a hyper scale data repository for enterprises to build cloud-based data lakes securely. In this session, we will show you how HDFS compatibility of Azure Data Lake Store seamlessly enables analytics workloads and gives them agility and security over and above what the framework offers.

From ingesting large volumes to storing them in native formats and then enabling analytics services munge over it at high throughput rates, you will see all data lake value props in action.

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U-SQL for Big Data - A Definitive Guide
Presented February 16, 2016

Making Big Data processing easy requires great developer support that hides the complexity of managing the scale, allows to easily integrate custom code to handle the complex processing requirements ranging from data cleanup to advanced processing of unstructured data, and provides great tool support for the developer to help in the iterative development process.

Thus when we at Microsoft introduced the Azure Data Lake, we decided to also include a new language called U-SQL to make Big Data processing easy. It unifies the declarative power of SQL and the extensibility of C# to make writing custom processing of Big Data easy. It also unifies processing over all data – structured, semi-structured and unstructured data – and queries over both local data and remote SQL data sources.

This presentation will delve deep on U-SQL, why we decided to build a new language, what its core design underpinnings are as well as show the language in its natural habitat – the development tooling – showing the language capabilities as well as the tool support from starting your first script to analyzing its performance.

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Azure Data Lake Analytics Deep Dive
Presented February 2, 2016

Azure Data Lake Analytics is a new distributed service in the Azure Data Lake.

In this session we’ll cover:

  • Basic components of the Azure Data Lake Analytics
  • Explore the architectural layers that support storage and compute
  • Recap U-SQL
  • How to understand patterns for organizing data and processing for big data pipelines
  • How to construct a basic ADLA pipeline that integrates with other Azure services such as Azure Data Factory

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What is Azure Data Lake?
Presented January 19, 2016

Big Data and Data Warehousing have taken a giant leap in the last few months and are now the helm of any data platform discussion.

This session delves into Microsoft’s play in the Big Data workload, and paints the end-to-end picture of effective solutions. Topics include: 

  • The importance of building a Data Lake
  • Nurturing its potential with Hadoop and other analytics options
  • Enriching the data warehouse with unforeseen tracts of information

This session will unfold an important chapter in the Microsoft Data Platform story.

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Data Science for the Rest of Us
Presented Dec 1, 2015

Do you wonder what data scientists do all day? Do you work with one? Are you thinking of becoming one?

This webinar is for you. Tune in for a math-free, jargon-free, pictorial explanation of what data science is all about.

We’ll pull the curtain back and reveal the most closely guarded tricks of the trade like

  • How to get data that isn’t useless
  • How to get answers to your most important questions
  • How to get other people to do your data science work for you

Join us for an informative session with Brandon Rohrer (@_brohrer_) creator of the Azure ML CheatSheet

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Using the Cortana Intelligence Process to Build Intelligent Applications
Presented Nov 10, 2015

In this talk, you will learn how the Cortana Intelligence Process empowers you with a systematic approach to understand your raw data, and transform it into actions you can embed in intelligent applications. We will walk through the Cortana Intelligence Process using a real-life large publicly available New York City (NYC) Taxi dataset that consists of 174+ million NYC taxi rides taken during year 2013. You will learn how to use JuPyter notebooks to explore the data to derive insights and use Azure Machine Learning to create predictive models that can power intelligent applications. You will learn how to operationalize your model as a web service, and integrate it with an application. Join us in this demo-packed session, as you learn how to design, build and operationalize these intelligent solutions powered by Cortana Intelligence Suite and much more.

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Cortana Intelligence Perceptual APIs
Presented October 27, 2015

Want to know how we built the HowOldRobot? Come learn about the Cortana Intelligence Perceptual APIs. These set of APIs allow you to quickly develop applications that require insights on the world around you, from streams such as text, audio, and images. These general-purpose machine learnt models have been used across many Microsoft first-party products and are now available as part of the Cortana Intelligence Gallery to power your applications. Computer Vision & Face APIs let your code understand and manipulate image content. Speech APIs enable you to communicate with users using audio, thanks to both speech recognition as well as speech synthesis. Text Analytics can help you understand what unstructured text is about, and identify sentiment and key phrases. LUIS brings natural-language understanding to any application through a simple model creation UX that relies on active learning to improve the model with use over time.

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Cortana Intelligence for Retail Pricing
Presented October 13, 2015

For retailers, price and demand are strongly correlated and are at the core of any successful strategy. When pursuing a revenue or profit maximization strategy, setting the right price level to the right products allows accurate prediction of future demand.

In the session we will explore how Cortana Intelligence Suite supports producing price elasticity curves, predicting demand for a given price point, and optimizing pricing strategy. You’ll be able to walk out with a good knowledge of data-driven pricing.

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Using Jupyter/IPython Notebooks in Azure ML
Presented September 2015

Jupyter notebooks (formerly IPython) provide a highly productive canvas for data scientists and developers to explore ideas. At its heart, Jupyter is a multi-lingual REPL (read eval print loop), where you can enter some code, and get a response. The response can be program output, a graph, etc. The notebook is comprised of interspersed code and markdown text for documentation purposes. For examples of some Notebooks, take a look at http://nbviewer.org.

We're delighted to announce the availability of Jupyter notebooks as a service on Azure ML. It is integrated with the Azure ML Studio, which means you can explore your datasets, write code, build models, etc. conveniently from a Notebook. Want to use Pandas or Seaborn to check out a data set, visualize it, slice/dice it and store it back? Simple: just click the data set in the Studio and select "Open in Notebook". Best of all there is no installation required. You can use Jupyter notebooks with AML from any modern browser from any OS.

In this webinar we'll cover what Jupyter notebooks are, the integration with AML Studio, and Operationalization of code to run on the AML backend.

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Real-time and Predictive Insights on Vehicle Health and Driving Behavior Pattern Using Cortana Intelligence
Presented September 2015

In this demo, we will enable you to setup and run a sample Cortana Intelligence application to light up an Internet of Things (IoT) usecase related to vehicle telemetry analysis. The analytics dashboard we create will allow car manufacturers, insurance companies or dealerships to gain real-time predictive insights on vehicle health and driving habits to enable improvements in the areas of personalized customer experience, R&D and marketing campaigns.

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Leveraging Predictive Analytics for Sales and Marketing
Presented September 2015

In this session, learn how the Microsoft Global Marketing team built predictive analytics solutions to meet the marketing and sales business need for Microsoft subsidiaries and business groups. The session will describe how Cortana Intelligence components (Azure Machine Learning, Azure Data Factory) are used to build e2e solution in Cloud.

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Harness Predictive Customer Churn Models with Cortana Intelligence Suite
Presented August 2015

In this session, learn how to build a real-life churn model with Azure Machine Learning, make it enterprise-ready with Azure Data Factory, and deliver data insights with Power BI.

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Introduction to Azure Data Factory
Presented August 2015

Data Factory enables you to process on-premises data like SQL Server, together with cloud data like Azure SQL Database, Blobs, and Tables. This session will help you jumpstart on understanding Data Factory capabilities, and the scenarios where Data Factory can be applied.

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Building Predictive Maintenance Solutions with Azure ML
Presented July 2015

Gaining attention largely due to the rise of the Internet of Things (IoT), predictive maintenance can be defined as a technique to predict when an in-service machine will fail so that maintenance could be planned in advance. However, the concept of predictive maintenance has evolved and covers a wide range of applications. Through a real-world example, I will show different ways of formulating a failure prediction problem. By showing a step-by-step procedure of data input, data preprocessing, data labeling and feature engineering to prepare the training/testing data on a publicly available dataset, I will present how convenient it is to build a predictive model in Azure ML and deploy it as a web service.

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Energy Forecasting in Smart Grids using Azure Machine Learning
Presented July 2015

In this session, you will learn about how we use Azure Stream Analytics to collect real time data; use Azure SQL to store data; use Azure Machine Learning to build a forecast model; use Azure Data Factory to automate the model and use PowerBI to visualize results on a dashboard. You’ll be able to walk out with the knowledge to create your own end to end energy forecasting solution.

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Azure Machine Learning for Software Engineers
Presented June 2015

Get a running into machine learning with this short introductory session about Azure Machine Learning, specifically intended for engineers. For many software engineers, machine learning and data science largely remains a mystery, even as the technology becomes more and more pervasive in business models, forecasting, predictive maintenance, and more.

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Create your end to end IoT solution using Azure Stream Analytics
Presented June 2015

In this session learn how to go from a sensor collecting live temperature, humidity etc. to dashboards, real time analytics, and real time alerting. Learn about all the various Azure services that will be created in order to enable your end to end solution. You will also see how you can use anomaly detection machine learning model over the sensor data. Walk out with the knowledge to create your IoT solution in the matter of hours.

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Deep Dive: Azure Stream Analytics Query Language
Presented May 2015

Azure Stream Analytics removes the complexity of stream processing for developers by providing a SQL-like language for authoring queries.
Learn how to build queries to perform data transformations such as filtering, aggregations over time windows, joining multiple streams together, correlating reference data (or static) with streaming data, and detecting patterns over data streams in real time. 
With live code demos over an Internet of Things (IoT) scenario, gain the knowledge needed to create your first stream processing job in minutes.

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How to use LifeData® APIs available on Azure Marketplace for Marketing Analytics
Presented May 2015

Versium, a data technology company, will discuss the power of LifeData®, why LifeData® APIs were made available on the Azure Marketplace, and how easily data can be ingested and used to optimize marketing. Versium uses industry leading matching technologies to blend LifeData® with individual enterprise customer records to deliver deep customer insights that help marketers with targeting, customer acquisition, segmentation, messaging, cross selling, and churn reduction.

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An Introduction to Azure Stream Analytics
Presented May 2015

Azure Stream Analytics, is an Azure Service that enables real-time insights over streaming data from devices, sensors, infrastructure, and applications. In this webinar, we will provide introduction to the service, common use cases, example customer scenarios, business benefits, demo of how to get started and quick build a simple real time analytic application.

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Learn How to Create Text Analytics Solutions with Azure Machine Learning Templates
Presented April 2015

In this webinar, we will focus on the Text Classification template. There are broad applications of text classification: categorizing newspaper articles and news wire contents into topics, organizing web pages into hierarchical categories, filtering spam email, sentiment analysis, predicting user intent from search queries, routing support tickets, and analyzing customer feedback.

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Overview - Azure Machine Learning Marketplace
Presented April 2015

The Azure Machine Learning Marketplace provides turn-key, end-to-end solutions, accessible to everyone - even those without a data science background. This webinar will discuss some of these solutions. We will focus on Recommendations, Customer Churn Prediction, and Text Analytics with demos on how to get started with each.

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Build Predictive Models with Microsoft’s Analytics Toolkit
Presented March 2015

In this webinar we will learn more about Microsoft’s Hadoop solution delivered in the cloud. Azure HDInsight will allow you to capture this new world of data and gain insights that were not possible before.

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Machine Learning for Business Users and Enterprise Developers
Presented March 2015 

This webinar will explore Machine Learning concepts and use cases in terms business users can understand. The panel will also discuss in concrete terms how Machine Learning can be used by mainstream developers and database professionals.

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Azure Machine Learning - An Overview of New Capabilities
Presented March 2015 

Azure Machine Learning (ML) has reached the GA milestone. In this webinar, we will explore the numerous new capabilities added in the GA release.

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The Cloud Data Science Process
Presented March 2015 

This webinar demonstrates the end-to-end data science process in the cloud, using the full spectrum of Azure technologies, programming languages such as Python and R, and other tools.

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Building Automated Data Pipelines
Presented February 2015 

This webinar covers common use-case driven strategies, architectures and the tools needed to build automated data pipelines for consuming trained AML models.

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Predictive Analytics with Azure Machine Learning
Presented December 2014 

Learn how you can build a predictive analytics model using machine learning over data and hear about real-world customer success stories.

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Operationalizing R as a Web Service
Presented November 2014 

R is the most widely used language today for machine learning, but its power is sometimes limited by gaps in the technology meant to bring it to life. In this on-demand webinar, learn how you can use your existing skills in R in new ways, including deploying models as web services with a few clicks.

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Hadoop in the Cloud
Presented September 2014

In this webinar we will learn more about Microsoft’s Hadoop solution delivered in the cloud. Azure HDInsight will allow you to capture this new world of data and gain insights that were not possible before.

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