AutoML tools are the need of the hour for data scientists to reduce their workloads in the world where the generation of data is only increasing exponentially. Readily available AutoML tools make the data science practitioner's work more comfortable and covers necessary foundations needed to create automated machine learning modules. And with the spur in data and the potential that this data holds, data scientists will benefit more by using AutoML capabilities. As we approach the midpoint of 2020, it is slowly being recognised that this year will see an increase in adaptation of AutoML. With the massive potential of AutoML about to burst, non-data science professionals and data science practitioners will look to get a more comprehensive view on the technology.

In July, VMware acquired Bitfusion, a company whose technology virtualizes compute accelerators with the goal of enabling modern workloads like artificial intelligence and data analytics to take full advantage of systems with GPUs or with FPGAs. Specifically, Bitfusion's software allows for virtual machines to offload compute duties to GPUs, FPGAs, or even other kinds of ASICs. The deal didn't get a ton of attention at the time, but for VMware, it was an important step in realizing its cloud ambitions. "Hardware acceleration for applications delivers efficiency and flexibility into the AI space, including subsets such as machine learning," Krish Prasad, senior vice president and general manager of VMware's Cloud Platform business unit, wrote in a blog post announcing the acquisition. "Unfortunately, hardware accelerators today are deployed with bare-metal practices, which force poor utilization, poor efficiencies, and limit organizations from sharing, abstracting and automating the infrastructure. This provides a perfect opportunity to virtualize them – providing increased sharing of resources and lowering costs."