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Microsoft Azure ML Franck Mercier Architecte Solutions | DX | Microsoft France @FranmerMS http://aka.ms/franck.

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Presentation on theme: "Microsoft Azure ML Franck Mercier Architecte Solutions | DX | Microsoft France @FranmerMS http://aka.ms/franck."— Presentation transcript:

1 Microsoft Azure ML Franck Mercier Architecte Solutions | DX | Microsoft

2 What is Machine Learning?
4/6/2017 4:57 PM What is Machine Learning? Delivering on one of the old dreams of Microsoft co-founder Bill Gates: Computers that can see, hear and understand. John Platt Distinguished scientist at Microsoft Research Computing systems that improve with experience So what is Machine Learning? Machine learning turns data into software. Data scientists create software which is trained from huge volumes of data. The software can consider far more variables than a human making the same decision. In a time when the quantity of data is doubling about every 18 months, machine learning can use all that data to solve your customers business problems. Let’s illustrate that with a machine learning solution that helps us all. © 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

3 What is Machine Learning?
4/6/2017 4:57 PM What is Machine Learning? The United States Postal Service processed over 150 billion pieces of mail in 2013—far too much for efficient human sorting. But as recently as 1997, only 10% of hand-addressed mail was successfully sorted automatically. Last year the United States Postal Service processed 150 Billion pieces of mail – far to much for efficient human sorting, but as recently as 1997, only 10% of all the hand-addressed mail was sorted automatically. Why? https://about.usps.com/who-we-are/postal-facts/size-scope.htm © 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

4 What is Machine Learning?
4/6/2017 4:57 PM What is Machine Learning? The challenge in automation is enabling computers to interpret endless variation in handwriting Because this is a tough problem – the type of problem machine learning is designed to solve. It has taken so many years to automate the sorting of the mail because reading handwriting is hard due to all the variables involved. Even humans have trouble reading other humans’ handwriting, if you can imagine the thousands of ways someone can write a name or address, this is a huge machine learning problem to solve. How can we teach the machine to read the mail and how can the machine learn and get better over time? © 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

5 What is Machine Learning?
4/6/2017 What is Machine Learning? Training examples Accurate digit classifier The answer is by providing feedback both in terms of humans training the machine learning models and the machine learning from the patterns in the data over time. By providing feedback, the Postal Service was able to train computers to accurately read human handwriting. This is where the “learning” part of machine learning comes in. Data scientists created a model based on all the data they had on how people can write addresses. Then they train the model as more data comes in, correcting attempts at reading handwriting when they’re off, until the model has enough of a history to draw from that it can accurately read handwriting. 1 5 4 3 7 9 6 2 Machine learning system 2 Training labels © 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

6 What is Machine Learning?
4/6/2017 4:57 PM What is Machine Learning? By providing feedback, the Postal Service was able to train computers to accurately read human handwriting. Today, with the help of machine learning, over 98% of all mail is successfully processed by machines. Today, with the help of machine learning, over 98% of all mail is successfully processed by machines. https://about.usps.com/who-we-are/postal-facts/size-scope.htm © 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

7 “ ” Microsoft & Machine Learning 20 years of realizing innovation
4/6/2017 4:57 PM Microsoft & Machine Learning 20 years of realizing innovation 1999 2004 2005 2008 2010 2012 2014 Computers work on users behalf, filtering junk Microsoft search engine built with machine learning SQL Server enables data mining of databases Bing Maps ships with ML traffic- prediction service Microsoft Kinect can watch users gestures Successful, real-time, speech-to- speech translation Microsoft launches Azure Machine Learning, making years of innovation available Back in the 90s when the post office was wrestling with this issue, we were also working on Machine Learning, starting in 1991 when Microsoft Research was formed. As early as 1999 they were using it to help create filters by predicting which s were junk, and which were relevant. And as John Platt mentions—it’s a key technology that Microsoft uses to develop its own software. In Machine learning was part of Microsoft’s search engine It is also used in Bing Maps as part of the traffic prediction service. And many people know about how it was a key technology to make Kinect a reality, letting computers track people’s gestures and sort through what’s relevant and what’s not. Like filtering out a dog in the background to see a player’s movements. And today, this technology that has been developed over decades is becoming available commercially as part of Azure It’s this depth of experience with machine learning, testing and refining over years, using it to develop pretty much all Microsoft products, that makes Microsoft’s solution so robust. Machine learning is pervasive throughout Microsoft products. John Platt, Distinguished scientist at Microsoft Research © 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

8 Azure Machine Learning: How it works
4/6/2017 4:57 PM Azure Machine Learning: How it works Azure Machine Learning offers a data science experience that is directly accessible to business analysts and domain experts, reducing complexity and broadening participation through better tooling. Hans Kristiansen Capgemini Enable custom predictive analytics solutions at the speed of the market Now let’s dive in to how Azure Machine Learning works, and talk about what’s unique about our offering. It’s easier and faster to use than anything out there—truly enabling custom predictive analytics solutions at the speed of the market. © 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

9 Machine learning today Complexities in a nascent market
4/6/2017 4:57 PM Machine learning today Complexities in a nascent market Huge set-up costs create unnecessary barriers to entry Break away from industry limitations With our solution you literally need just a Microsoft Azure subscription Siloed and cumbersome data management restricts access to data Our ML solution contains its own storage space in the cloud and is seamlessly connected to HDInsight, SQL Server in a Virtual Machine, SQL Database, Blobs and Tables But as of now the power of machine learning is only being utilized by a very small group, requiring highly trained and specialized data scientists to create and nurture models for them to be useful. In this young market, there are still many complexities. First, the huge investment necessary to even get started with machine learning keeps most companies out completely. Both the technology itself and the specialized team needed to make it work are just too expensive. With our solution you literally need just a Microsoft Azure subscription. Second, the data management side can be really cumbersome, posing unnecessary limitations on what data can be included in models. Our ML solution contains its own storage space in the cloud and is seamlessly connected to HDInsight, SQL Server in a Virtual Machine, SQL Database, Blobs and Tables. Third, collaborating across technologies, let alone geographies, is difficult and limits participation in exploring data and building predictive models. It’s just not that easy to use, so people end up struggling with the technology instead of focusing on the business problem at hand. With our solution data scientists can share their workspaces with colleagues securely with a click. Finally, and maybe most discouraging considering the investments involved, many models never achieve business value because it’s so hard to deploy them to production. Imagine spending hundreds of thousands of dollars on a solution and having it never see the light of day. You can see why machine learning has been so niche up to this point. Most revolutionary of all, our models can be deployed to production in minutes, allowing data scientists to show value to the business at the speed of the market. Microsoft has a head start. Our scientists have been building and testing models in our own technology for two decades. And through that time, they have been making it easier and easier to use. It’s thrilling to see this become available commercially. These market limitations don’t need to be a reality any more. Complex and fragmented tools limit participation in exploring data and building predictive models With our solution data scientists can share their workspaces with colleagues securely with a click. Many models never achieve business value due to difficulties with deploying to production Most revolutionary of all, our models can be deployed to production in minutes, allowing data scientists to show value to the business at the speed of the market. © 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

10 One solution for Machine Learning — from data to results
4/6/2017 4:57 PM One solution for Machine Learning — from data to results Business users easily access results: from anywhere, on any device PowerBI/ Dashboards Web Apps Mobile Apps ML API service and the Developer Tested models available as an url that can be called from any end point Here’s a simplified snapshot of the whole solution, from storing and managing data, to business users accessing results and making decisions. If you already have a Microsoft Azure subscription or data in the cloud – especially in HDInsight – you are more than halfway there to realizing the benefit of this solution. Let’s start in the bottom left with the Azure Portal. The Azure ops team, maybe already accustomed to managing storage accounts or provisioning Azure virtual machines, can get a machine learning environment set up right from the Azure Portal. They can: Create an ML Studio workspace and dedicated storage account to get their data scientists up and running Monitor ML consumption to keep track of expenses See alerts when a model is ready to be published And deploy models as web services with the ML API Service Now, moving right, to the ML Studio experience. This where the data scientist will spend her time: She can execute every step in the data science workflow in one place – ML Studio She can access and prepare data Create, test and train models, as well as import her company’s proprietary models securely into her private workspace Work with R and over 300 of the most popular R packages along with Microsoft’s business class algorithms Collaborate with colleagues within the office or across the globe as easy as clicking “share my workspace” Deploy models within minutes rather than weeks or months And the data scientist has her choice of what data she wants to pull into her models. She can access data already in Azure, query across Big Data in HDInsight, or pull datasets in right from her desktop. Once the data scientist is ready to publish, that’s when tested models become available to developers via the API service. The business users can access results, from anywhere, on any device. And any model updates simply refresh the model in production with no new development work needed. Azure Portal & ML API service and the Azure Ops Team Create ML Studio workspace Assign storage account(s) Monitor ML consumption See alerts when model is ready Deploy models to web service ML Studio and the Data Scientist Access and prepare data Create, test and train models Collaborate One click to stage for production via the API service HDInsight Azure Storage Desktop Data © 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

11 Requires Agile Experimentation
Data Science Process Requires Agile Experimentation Define Objective Define the objective and quantify it with a metric – optionally with constraints, if any. Collect and understand the data, deal with vagaries and biases in data acquisition (missing data, outliers due to errors in data collection process, more sophisticated biases due to the data collection procedure, etc. Frame the problem in terms of a ML problem – classification, regression, ranking, clustering, forecasting, outlier detection etc. Access and Understand the Data Pre-processing Feature and/or Target construction Transform raw data into a “modeling dataset”, with features, targets etc., which can be used for modeling. Feature construction often improved with domain knowledge. Target must be identical or proxy of metric in step 1.

12 Requires Agile Experimentation
Data Science Process Requires Agile Experimentation Train/ Test split Train, test and evaluate, taking care to control bias/variance and ensure the metrics are reported with the right confidence intervals (cross-validation helps here), be vigilant against target leaks (which typically leads to unbelievably good test metrics). Feature selection Model scoring Model training Evaluation

13 Requires Agile Experimentation
Data Science Process Requires Agile Experimentation Iterate steps (2) – (5) until the test metrics are satisfactory Define Objective Train/ Test split Access and Understand the data Feature selection Model scoring Pre-processing Model training Evaluation Feature and/or Target construction

14 “ ” azure.com/ml Learn more and sign up for a free trial of Azure
4/6/2017 4:57 PM La suite Azure ML is the future of Analytics. It seamlessly brings together statistics/mathematics with ML, AI, and advances in data storage and computing. Corey Coscioni West Monroe Partners, LLC Learn more and sign up for a free trial of Azure azure.com/ml © 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

15 Azure ML


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