H2O.ai Blog
June 7th, 2018
H2O.ai and IBM build a Strategic Partnership to bring AI innovation to the market together
RSS
Share
Category: Uncategorized [EN]
By: Sri Ambati
Excited to announce our strategic partnership with IBM that allows them to resell and take to market H2O Driverless AI to businesses worldwide. This partnership makes AI economical – faster, cheaper and easier to do experiments. H2O Driverless AI and IBM POWER9 GPU Systems are bringing together the best of breed AI innovation. We have been working with IBM to port Driverless AI, our latest product that addresses the skills gap in data science and trust in AI, to IBM Power Systems. The combination delivers 5X performance for workloads including the new time series in Driverless AI. Please check out the blog by Sumit Gupta, VP of AI, Machine Learning and HPC at IBM Cognitive Systems, for more details on the partnership.
Outstanding performance on IBM POWER9
HPC is the new PC. To handle the increasingly complex workloads of AI you need an integrated system of software and hardware that is fully optimized for each other. H2O Driverless AI on IBM POWER9 delivers precisely that. IBM POWER9 supports nearly 2.6x more RAM, 9.5x more I/O bandwidth than comparable systems. It can also support upto 6 V100 GPUs on a single system. Driverless AI is built on top of datatable for python for data ingest and feature engineering and H2O4GPU for machine learning. We’ve been able to get nearly 2X the data ingest speed and over 50% faster feature engineering. In addition, with the power of GPU accelerated machine learning we’re able to deliver nearly 30X speedup on model building. Overall, we’ve been able to accelerate Driverless AI by up to 10X for IID data and up to 5X for time series data.
The Power of IBM and H2O.ai Solves Customer Challenges
AI will transform businesses as we know it. Companies will leverage AI across multiple business units and establish centers of excellence for AI. From asset price forecasting in capital markets, to supply chain optimization in manufacturing, to personalized insurance policies, no walk of business will be immune to pressures to democratize decision making with AI. IBM and H2O.ai will build the trusted hardware / software co-design needed for enterprises to make that transition. The winners of this partnership are our customers and their stories will be replicated across the ecosystem.
this will be fun, Sri
About the Author
Sri Ambati
Sri Ambati is the CEO and Founder of H2O.ai where he has launched a growing movement to democratize AI for everyone. Sri is regularly consulted by business leaders to help develop strategies to help them embark on an AI transformation. He has successfully combined his strong vision to “Make Your Company an AI Company” with impressive technical credentials to help organizations in all industries – from finance to healthcare to retail and manufacturing – maximize the impact of their data on business results, and improve day-to-day life for people around the world. Sri was recently featured in an article by IDG Connect where he discussed his vision for ubiquitous AI. He has also been featured on CNBC and the WSJ discussing his AI vision, and was named a Datanami Top People to Watch in 2019. He is a regular speaker at AI, machine learning and big data conferences such as AI4Finance, Big Data LDN and H2O World. Prior to founding H2O.ai, Sri co-founded big data analytics startup Platfora (acquired by Workday), and held technical and executive roles at Datastax, Azul Systems and RightOrder. His academic career includes sabbaticals focused on theoretical neuroscience at Stanford and UC Berkeley. He holds an M.S. in math and computer science from the University of Memphis.
H2O LLM DataStudio Part II: Convert Documents to QA Pairs for fine tuning of LLMs
Convert unstructured datasets to Question-answer pairs required for LLM fine-tuning and other downstream tasks with
September 22, 2023 - by Genevieve Richards, Tarique Hussain and Shivam Bansal
Building a Fraud Detection Model with H2O AI Cloud
In a previous article[1], we discussed how machine learning could be harnessed to mitigate fraud.
July 28, 2023 - by Asghar Ghorbani
A Look at the UniformRobust Method for Histogram Type
Tree-based algorithms, especially Gradient Boosting Machines (GBM's), are one of the most popular algorithms used.
July 25, 2023 - by Hannah Tillman and Megan Kurka
H2O LLM EvalGPT: A Comprehensive Tool for Evaluating Large Language Models
In an era where Large Language Models (LLMs) are rapidly gaining traction for diverse applications,
July 19, 2023 - by Srinivas Neppalli, Abhay Singhal and Michal Malohlava
