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4 Examples of Connecting Data To ML and AI

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4 Examples of Connecting Data To ML and AI

Let's take a look at connecting data to Machine Learning and Artificial intelligence through 4 examples.

· AI Zone ·
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Java-based (JDBC) data connectivity to SaaS, NoSQL, and Big Data. Download Now.

Data that has been collected, collated, and cleansed is ripe for analysis and insight generation. Advances in Machine Learning and AI are helping deliver on the promises of augmented analytics to produce actionable insights. Pairing Machine Learning techniques with prepared data enables organizations to achieve more accurate predictions and measurable analysis on all kinds of business functions.

A growing number of BI and Analytics tools vendors are responding to the need for augmented BI by opening their platforms through APIs and making stored data more easily accessible. This is a critical first step that gives IT the ability to build connections from Machine Learning products to raw, cleaned, and prepared data.

The promises of AI and ML are exciting, and our engineering teams have worked hard to streamline connectivity between our drivers and popular augmented intelligence platforms. Standards-based drivers greatly simplify integration with ML, providing a consistent and reliable interface for consuming real-time data. We started with our open source ODBC Reader for TensorFlow, and have since worked with a wide array of modern Machine Learning products and platforms to make sure that users have an optimal experience.

Check out some of the examples below to learn more about connecting leading Machine Learning solutions to real-time data through our drivers:

JDBC Drivers and H2O

H2O is an open source, in-memory, distributed, fast, and scalable Machine Learning and predictive analytics platform that allows enterprises to build Machine Learning models on big data and provides easy productionalization of those models in an enterprise environment. The CData JDBC Drivers can be used in R or Python to import data into H2O and build Generalized Linear Models (GLMs), enabling rapid connectivity to enterprise data from H20, no matter where it is.

To connect to data in H20 via JDBC:

  1. Run H20, adding the JAR file from any of the 100+ JDBC Drivers to the class path.
  2. Use the  import_sql_table  method in R or Python to import data.
  3. Build a GLM using H2O functions.
  4. Work with live enterprise data in H20, training, validating, predicting, etc.

JDBC Drivers and KNIME

KNIME Analytics Platform helps enterprises discover the potential hidden in data, mine for fresh insights, or predict new futures. When paired with CData JDBC drivers, KNIME can access enterprise data from any of the 100+ supported Big Data, NoSQL, and SaaS sources.

To connect to live data in KNIME via JDBC:

  1. Create a new database node based on the JDBC driver.
  2. Add a Database Reader to the workflow and configure the Reader.
  3. Connect the Database Reader to a Data to Report node.

JDBC Drivers and RapidMiner

RapidMiner Studio is a visual workflow designer that makes data scientists more productive, from the rapid prototyping of ideas to designing mission-critical predictive models. The CData JDBC Drivers can be paired with RapidMiner to greatly expand the options for data connectivity, freeing enterprises to work with all of their data without the need for replication or integration development.

To work with live data in RapidMiner:

  1. Add a new database driver based on the CData JDBC Driver.
  2. Create a new database connection based on new database driver.
  3. Use the new connection with various RapidMiner operators

ODBC Drivers and Alteryx

Alteryx Designer empowers data analysts by combining data preparation, data blending, and analytics - predictive, statistical and spatial - using the same intuitive user interface. Utilizing the CData ODBC Drivers in Alteryx Designer offers deeper connectivity and widens the opportunities for self-service analytics on all enterprise data.

To work with live data in Alteryx:

  1. Configure a DSN to connect to any of the 100+ supported data sources.
  2. Connect a data input tool to the DSN.
  3. Use the data input tool in a workflow to prepare, blend, and analyze data.

Let me know your thoughts in the comments section!

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Topics:
cdata software ,data connectivity ,odbc ,ai ,jdbc ,machine learning

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