In Machine Learning, you work with data in the form of datasets, which are tables of data with accompanying metadata that describes the schema and analytical usage of each column. The modules provide many tools that are important and useful in data science, including a variety of leading-edge machine learning algorithms. Other modules provide data entry functions, tools for transforming data for statistical analysis, and support for machine learning workflows such as training, scoring, and validation processes. The following image shows a typical experiment with multiple modules. One module is used to choose a subset of columns (), another creates a linear regression model to predict a numeric outcome (), and successive modules train the model () and generate predictions using a trained regression or classification model (). Image based on article “” For more information about Microsoft Azure Learning, see these articles. () For tutorials, templates, and pre-developed solutions and walkthroughs for a variety of scenarios, see the following. Azure Machine Learning Studio includes many sample datasets that have been pre-configured to make analysis easier. () Stream Analytics Azure Stream Analytics provides complex event processing over streaming data in the cloud. The processing is low-latency, highly available with built-in recovery and checkpointing capabilities, and scalable. You can combine streams of data with historic records or reference data to derive business insights. The key scenarios are: Perform complex event processing on high-volume and high-velocity data Collect event data from globally distributed assets or equipment, such as connected cars or utility grids Process telemetry data for near real-time monitoring and diagnostics Capture and archive real-time events for future processing
In Machine Learning, you work with data in the form of datasets, which are tables of data with accompanying metadata that describes the schema and analytical usage of each column. The modules provide many tools that are important and useful in data science, including a variety of leading-edge machine learning algorithms. Other modules provide data entry functions, tools for transforming data for statistical analysis, and support for machine learning workflows such as training, scoring, and validation processes. The following image shows a typical experiment with multiple modules. One module is used to choose a subset of columns (), another creates a linear regression model to predict a numeric outcome (), and successive modules train the model () and generate predictions using a trained regression or classification model (). Image based on “” article For more information about Microsoft Azure Learning, see these articles. () For tutorials, templates, and pre-developed solutions and walkthroughs for a variety of scenarios, see the following. Azure Machine Learning Studio includes many sample datasets that have been pre-configured to make analysis easier. () Stream Analytics Azure Stream Analytics provides complex event processing over streaming data in the cloud. The processing is low-latency, highly available with built-in recovery and checkpointing capabilities, and scalable. You can combine streams of data with historic records or reference data to derive business insights. The key scenarios are: Perform complex event processing on high-volume and high-velocity data Collect event data from globally distributed assets or equipment, such as connected cars or utility grids Process telemetry data for near real-time monitoring and diagnostics Capture and archive real-time events for future processing
introducing_microsoft_bi_tools.docx