Accelerate AI/ML and Data Science Projects with AI-Powered Modern Data Prep

AWS has recently announced a new type of instance targeted at I/O intensive applications, the i3.metal. That instance does away with the virtualization layer altogether, and gives back the resources that would otherwise be used by the hypervisor back to the application.To use all of those resources — 72 CPUs and 512GB of memory — a database needs to be have the ability to scale both up and out.

Performance Optimization and Troubleshooting Modern Data Applications With cloud becoming the deployment platform of choice for data pipelines, many IT organizations must now come to grips with what that means for planning, budgeting, migrating and operating big data in the cloud.

According to industry analysts, more than 85% of artificial intelligence (AI) deployments never make it into production. Moving from research (using small data sets) to robust online production environments is far from trivial. Production environments need to process large amounts of real-world data and meet application performance and SLA goals. To bring AI projects from the lab to production, organizations need a production-first mindset. They also need underlying infrastructure built for scale and high performance.

Even as there is a growing recognition of Artificial Intelligence (AI) transformative impact in enhancing operational efficiency, organizations still struggle with the widespread adoption of this technology. In this video interview, Lindsay Demspey from Enmax Energy provides his insights into kicking off his AI journey. "We're looking for opportunities like this, and we've tried different technologies here and there throughout time, and some we pick up and take forward and some we don't. I was 100% sure before going to the pilot that I've got all of the needed data and could be accessed easily," said Lindsay Dempsey, Senior Lead Optimization Engineer, Enmax Energy Corporation.