Learn how to become data-driven

Explore a case study that shows how IBM Cloud Pak for Data with IBM Watson Machine Learning Accelerator helps the client toward the goal of being a data-driven organization. Learn how they leveraged an integrated data and AI platform to enable multiple business units, democratizing data science in the process. Now they are able to manage and analyze departmental and high-capacity (billions of rows) data with a single platform.

Exploration of the client goals, solution architecture, and use cases

A large energy company wanted to reinvent themselves as a data-driven organization, where advanced analytics and data science capabilities are readily accessible across multiple business units. The challenges they face involve siloed data, inconsistent tools, unknown skills requirements, and low confidence within the business units. In order to roll out the project, phases were defined covering platform requirement gathering, architecting a solution and deploying it and implementing use cases.

Watson Studio provides a common set of analytic tools to establish a data-driven explainable environment. The challenges that the enterprise faced included a skills gap with limited collaboration across teams, challenges with operationalizing trusted AI, and the need for an open and flexible multicloud environment. Building AI with Watson Studio makes it easy to deploy anywhere with IBM Cloud Pak for Data.

IBM Watson Machine Learning Accelerator and demos

From using GPUs to build state-of-the-art AI models to understanding whether you are using those GPUs efficiently, the IBM Watson Machine Learning Accelerator can monitor usage and be accessed through popular open platforms for AI development, such as Jupyter Notebooks.

Summary and next steps

Want to learn more about IBM Cloud Pak for Data? Check out the IBM Cloud Pak for Data hub on IBM Developer. Explore articles, step-by-step tutorials, and code patterns that explain the process of working with data using this unified, pre-integrated data and AI platform that runs natively on Red Hat OpenShift Container platform, and on IBM Cloud, Amazon Web Services (AWS), and Microsoft Azure.

You can also learn more about machine learning at the IBM Developer Machine learning hub.