1How GPU-Accelerated Databases Are Helping Advance Cognitive Computing
Artificial intelligence has been around in different forms for decades, but only now is it truly coming of age. AI experiments have made it out of the lab and are now being deployed into the real world by leading technology companies. For example, Amazon has smarter buying recommendations, Facebook has automatic facial recognition and Netflix offers movie recommendations. These things are possible because more horsepower is now available. Graphics processing units are helping provide much of the new power required to run these data-intensive apps. This eWEEK slide show offers some expert industry data points on the topic from Amit Vij, CEO and co-founder of database maker Kinetica.
2AI Needed to Improve CX, Operational Efficiency, Sales
Opportunities abound for AI, such as helping provide a more complete customer experience (CX) for shoppers and enabling buying recommendations for online retailers, judging the risk of various trade decisions at a bank, accelerating drug development in life sciences or providing smarter planning for logistics companies.
3Deploying AI in Business Isn’t Easy, but That Shouldn’t Stop Anybody
Data scientists typically rely on costly, complex and specialized tools and hardware. They frequently need to copy large volumes of data into these specialized environments to build and train their models. Once the AI models are working in the lab, it can be difficult to make that functionality available to business users. Finally, it’s difficult to find, train and retain the people with the right skills to develop, deploy and manage AI models.
4A GPU-Accelerated Database Can Converge AI and BI
GPU acceleration, which has been developed and optimized for years in the videogame world, immediately brings a 10X to 100X performance boost to business analytics. With streaming data and increasingly large datasets, many business users suffer from slow performance and complexity of their analytics solutions. GPU acceleration brings huge performance improvements and simplifies IT infrastructure for faster time to value.
5Developing AI Models Using Live Data
Data scientists can develop AI models on live data via user-defined functions. A GPU database is especially fast at the matrix and vector operations common with AI workloads. User-defined functions can call out to third-party machine learning/deep learning libraries such as BIDMach, Caffe and TensorFlow.
6GPU Database Can Extract Data From Disparate Systems
7A GPU-Accelerated Database Can Deploy AI Algorithms Faster
AI algorithms can be made available to business users in much less time. Using a tool such as Kinetica, for example, business tools can call UDFs directly through a REST API. This makes it possible to make custom algorithms available to business tools through easy-to-use, point-and-click interfaces.
8CPUs Have Become a Bottleneck in Many Systems
The CPU has become the bottleneck for many large analytics systems, resulting in server sprawl for handling complex analytical workloads. GPU-accelerated databases improve efficiency and customers have been able to reduce their hardware footprint to one-fourth or one-tenth the size of conventional analytics systems.
9GPU Databases Could Be the Next Big Thing
Hardware costs can be connected to business results. The benefits of investment in hardware for AI can be seen quicker when that hardware is made available to the business. GPUs are gaining acceptance as the next big thing for the data center and are now available on the cloud platforms such as AWS, Azure and Google Cloud.