Data Mining Concepts 6. Deploying and Updating Models

Deploying and Updating Models

Deploying and Updating Models The last step in the data mining process, as highlighted in the following diagram, is to deploy the models that performed the best to a production environment. +

After the mining models exist in a production environment, you can perform many tasks, depending on your needs. The following are some of the tasks you can perform: +

Use the models to create predictions, which you can then use to make business decisions. SQL Server provides the DMX language that you can use to create prediction queries, and Prediction Query Builder to help you build the queries. For more information, see Data Mining Extensions (DMX) Reference. Deploying and Updating Models The last step in the data mining process, as highlighted in the following diagram, is to deploy the models that performed the best to a production environment. +

After the mining models exist in a production environment, you can perform many tasks, depending on your needs. The following are some of the tasks you can perform:

• Use the models to create predictions, which you can then use to make business decisions. SQL Server provides the DMX language that you can use to create prediction queries, and Prediction Query Builder to help you build the queries. For more information, see Data Mining Extensions (DMX) Reference.

• Create content queries to retrieve statistics, rules, or formulas from the model. For more information, see Data Mining Queries.

• Embed data mining functionality directly into an application. You can include Analysis Management Objects (AMO), which contains a set of objects that your application can use to create, alter, process, and delete mining structures and mining models. Alternatively, you can send XML for Analysis (XMLA) messages directly to an instance of Analysis Services. For more information, see Development (Analysis Services - Data Mining).

• Use Integration Services to create a package in which a mining model is used to intelligently separate incoming data into multiple tables. For example, if a database is continually updated with potential customers, you could use a mining model together with Integration Services to split the incoming data into customers who are likely to purchase a product and customers who are likely to not purchase a product. For more information, see Typical Uses of Integration Services.

• Create a report that lets users directly query against an existing mining model. For more information, see Reporting Services in SQL Server Data Tools (SSDT).

• Update the models after review and analysis. Any update requires that you reprocess the models. For more information, see Processing Data Mining Objects.

• Update the models dynamically, as more data comes into the organization, and making constant changes to improve the effectiveness of the solution should be part of the deployment strategy. For more information, see Management of Data Mining Solutions and Objects