Presentation on theme: "Microsoft Azure ML Franck Mercier Architecte Solutions | DX | Microsoft"— Presentation transcript:
Microsoft Azure ML Franck Mercier Architecte Solutions | DX | Microsoft France @FranmerMS http://aka.ms/franck
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 “ ” 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.
The challenge in automation is enabling computers to interpret endless variation in handwriting What is Machine Learning?
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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. What is Machine Learning?
SQL Server enables data mining of databases Computers work on users behalf, filtering junk email Microsoft Kinect can watch users gestures Microsoft launches Azure Machine Learning, making years of innovation available Microsoft search engine built with machine learning Bing Maps ships with ML traffic- prediction service Successful, real-time, speech-to- speech translation Microsoft & Machine Learning 20 years of realizing innovation John Platt, Distinguished scientist at Microsoft Research 1999201220082004201420102005 Machine learning is pervasive throughout Microsoft products. “ ”
Enable custom predictive analytics solutions at the speed of the market 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 “ ” Azure Machine Learning: How it works
Machine learning today Complexities in a nascent market Break away from industry limitations Huge set-up costs create unnecessary barriers to entry Siloed and cumbersome data management restricts access to data Complex and fragmented tools limit participation in exploring data and building predictive models Many models never achieve business value due to difficulties with deploying to production With our solution you literally need just a Microsoft Azure subscription 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 With our solution data scientists can share their workspaces with colleagues securely with a click. 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.
One solution for Machine Learning — from data to results 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 Business users easily access results: from anywhere, on any device HDInsight Desktop Data Azure Storage Mobile Apps PowerBI/ Dashboards Web Apps ML API service and the Developer Tested models available as an url that can be called from any end point 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
Data Science Process Requires Agile Experimentation Define Objective Access and Understand the Data Pre-processing Feature and/or Target construction 1.Define the objective and quantify it with a metric – optionally with constraints, if any. 2.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. 3.Frame the problem in terms of a ML problem – classification, regression, ranking, clustering, forecasting, outlier detection etc. 4.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.
Data Science Process Requires Agile Experimentation Feature selection Model training Model scoring Evaluation Train/ Test split 5.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).
Data Science Process Requires Agile Experimentation Define Objective Access and Understand the data Pre-processing Feature and/or Target construction Feature selection Model training Model scoring Evaluation Train/ Test split 6.Iterate steps (2) – (5) until the test metrics are satisfactory
Learn more and sign up for a free trial of Azure azure.com/ml “ 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 ” La suite