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As much as we talk about the power of deep learning, most companies are still warming up to the technology. There’s no denying the promise it offers, but realizing business value can be challenging.
We’re about to change that, making it easier for leaders to harness insights with deep learning from the deluge of data.
Today, we’re excited to announce the union of IBM’s PowerAI with the IBM Data Science Experience (DSX), designed so that companies will be able to utilize leading open source DL frameworks like TensorFlow and Caffe with accelerated systems to democratize insights through AI. This is expected to result in better client experiences, new business models and more.
The PowerAI deep learning enterprise software distribution is integrating into the Data Science Experience, a collaborative workspace that helps data scientists to build, manage and deploy AI models. The PowerAI libraries and algorithms are optimized for the IBM Power Systems S822LC for High Performance Computing, which harnesses the industry’s only CPU to GPU NVIDIA NVLink high-speed interconnect, designed to provide outstanding accelerated deep learning.
Users ranging from data scientists to business analysts can now engage in machine and deep learning with the Data Science Experience collaborative environment. Data scientists are particularly well-positioned to look at deep learning to leverage data as a competitive differentiator and asset.
When it comes to deep learning, faster is better – and that’s why the PowerAI integration with the Data Science Experience is built to allow enterprise clients to tap into the unlimited potential of AI. It’s never too late to integrate deep learning into your enterprise, and IBM is committed to unlocking new ways for companies to evaluate data and do business. PowerAI with the Data Science Experience will only get better, and we look forward to sharing more details on enhancements in the future.
Learn more about the Data Science Experience, PowerAI and IBM Power Systems S822LC for High Performance Computing.