Welcoming H2O Driverless AI to the PowerAI ecosystem
There is the old saying that “it takes a village,” and that couldn’t be more true when it comes to building the best solutions for companies on the journey to AI. No one company has all the answers and with new technologies emerging daily, it is important to partner with the right trailblazers.
IBM Power Systems is committed to bringing together the best and brightest innovators in both hardware and software. For several years, we have collaborated closely with many companies to develop industry-leading accelerated hardware like the AC922 and Mellanox to deliver high performance networking in our infrastructure stack. We also work with software leaders like Hortonworks, MongoDB and EnterpriseDB Postgres to optimize their software for Power Systems servers. And now, we are working with cutting-edge AI leaders like Anaconda, Galvanize, ScyllaDB, MapD and Nimbix as well. Many of them had fantastic things to say about the Power Ecosystem in a recent blog post by IBM VP of Cognitive Systems Software, Tim Vincent.
On June 7th, I was excited to see the announcement that enterprises on the journey to AI have a new arrow in their quiver with the addition of automatic machine-learning platform H2O Driverless AI to the IBM Power Systems ecosystem.
Joining next generation artificial intelligence with IBM Power Systems has created a solution for organizations looking to take advantage of the growing machine learning and deep learning fields. With up to 5.6x more I/O throughput and 4x more threads than its compared x86 competitors,[1] Power Systems are capable of supporting the intense data processing and memory requirements that machine learning and deep learning workloads demand.
With the addition of H2O Driverless AI, adding machine learning insight to your business has gotten much easier. According to H2O.ai, it serves a network of over 100,000 data scientists with its open-source machine learning platform, H2O.
Driverless AI removes many of the significant barriers that prevent organizations from adopting machine learning. According to H2O.ai, H2O Driverless AI removes the need to do extensive and costly feature engineering upfront, in addition to automating model validation and tuning.
Businesses that want to analyze huge data pools of textual and numeric data can take advantage of serious performance upticks when pairing H2O Driverless AI with IBM Power Systems infrastructure.
Learn more about PowerAI and H2O Driverless AI.
[1] PCIe gen 4 provides 2x PCIe gen 3 bandwidth. NVLink provides 5.6x data throughput. OpenCAPI is engineered to deliver in excess of 2x data throughput. P9 cores provide 4x threads of x86 cores
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