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IBM Cloud Pak for Data

IBM Cloud Pak for Data is an integrated, flexible and open Data Science & AI platform for collecting, organizing, analyzing data and producing and monitoring analytical models.

CP4D is a set of three pre-built, pre-sized offerings designed to address problems in cataloging, analyzing and integrating data.

If you want to become a data-driven enterprise, Cloud Pak for Data should be at the center of your data and analytics ecosystem!

Cloud Pak for Data provides a modern data and analytics architecture that is elastic, scalable, and reliable. The end-to-end platform means that you can spend less time managing your data and more time using it to grow your business.

Your data isn’t static. Your machine learning models shouldn’t be static either. As data is added to your on-premises and cloud data sources, you need to continually test and tune your machine learning models to ensure that they give you valuable insight. But you need to make sure that you’re working with high-quality data, which is where the data governance and data integration and preparation services that you can install on Cloud Pak for Data come in.

You know the old adage: Garbage in, garbage out. If your data is poor, your results aren’t meaningful. By bringing data stewards and data engineers together with your data scientists, you can ensure that your data is ready for analysis.

You can choose which services you install on Cloud Pak for Data, so that you can use your resources wisely. Whether you want to modernize your data landscape, generate real-time insights to drive business transformations, or deliver exceptional, AI-augmented customer experiences, CP4D has a solution that can propel your business forward.

With the capabilities of the Cloud Pak for Data data fabric architecture, you can:

  • Simplify and automate access to data, across multi-cloud and on-premises data sources, without moving data.
  • Universally safeguard the use of all data, regardless of source.
  • Provide business users with a self-service experience for finding and using data.
  • Use AI-powered capabilities to automate and orchestrate the data lifecycle.

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