Azure Machine Learning: From design to integration Peter Myers M355.

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Presentation transcript:

Azure Machine Learning: From design to integration Peter Myers M355

Machine Learning Subfield of computer science and statistics that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions -Wikipedia

f() num1, num2 I need to add two numbers together…

I need to predict customer profitability… f() Age, Marital Status, Gender, Yearly Income, Total Children, Education, Occupation, Home Owner, Commute Distance

Define Objective Collect Data Prepare Data Train Models Evaluate Models PublishManageIntegrate

Strategic change Lots of buzz words New markets High competition DATA SCIENTIST Expensive Isolated data Tool chaos Complexity Consequences Lost opportunities Expensive operative mistakes Traditional approach Guessing Rules of thumb Trial and error

Azure Portal ML Studio ML API service Azure Ops team Data professionals & Data scientists Software developers

Define Objective Collect Data Prepare Data Train Models Evaluate Models PublishManageIntegrate

Define Objective I need to predict customer profitability…

Collect Data

Prepare Data

Train Models Evaluate Models

Publish

Manage

Integrate

Ad targeting Equipment monitoring Spam filtering Churn analysis Recommendation s Fraud detection Image detection & classification Forecasting Anomaly detection Imagine what you could use Machine Learning for…

Azure Portal Azure Ops Team ML Studio Data Professional HDInsightAzure StorageDesktop Data Azure Portal & ML API service Azure Ops Team Power BI/DashboardsMobile AppsWeb Apps ML API service Application Developer

Azure Portal Azure Ops Team ML Studio Data Scientist HDInsightAzure StorageDesktop Data Azure Portal & ML API service Azure Ops Team Power BI/DashboardsMobile AppsWeb Apps ML API service Developer ML Studio and the Data Professional Access and prepare data Create, test and train models Collaborate One click to stage for production via the API service 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 API service and the Application Developer Tested models available as a URL that can be called from any endpoint Business users easily access results from anywhere, on any device

Quick and easy extensibility with cloud functions such as Power BI, Hadoop (Azure HDInsight) and cloud storage

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