Ayush Parashar: Cloud has been a big factor in helping businesses to innovate.
To compete, especially around AI right now, it’s critical for a company to iterate on the latest and greatest tooling that can scale up and scale down in a commodity environment instantly.
All the major cloud providers have already innovated on their cloud offerings around AI and that has made the AI algorithm available in a ubiquitous fashion.
In addition to tooling and infrastructure around AI, data management and analytic tools are very important to take AI to the next level.
Getting the right data and integrating data from various places, cleansing it and preparing it is pivotal, and it’s often the first step that a data scientist works on for their AI project.
On premises doesn’t always provide this agility.
Organizational teams can become more successful if they can do more in less time.
Cloud allows for more tools to come together, faster, at scale.
insideBIGDATA: How will AI-optimized hardware solve important compute and storage requirements for AI, machine learning, and deep learning?.Ayush Parashar: Compute requirements are very important for any AI, ML and deep learning tasks.
GPUs have made a huge impact on the compute aspect, however, intelligence is moving toward the edge and by definition that is AI.
It’s going to make rapid innovations possible when it’s additive to the chip.
As AI and ML move towards edge compute models, specialized hardware will soon disrupt and play a big role.
It’s easy to envision the use of AI specialized chips in smartphones and home consumer devices of every form including refrigerators, ovens and cars.
It’s exciting to see innovation around AI chips including Neural Network Processors, FPGAs, Neuromorphic Chips.
From a storage perspective, the biggest impact has been made by use of SSDs.
insideBIGDATA: What’s the most important role AI plays for your company’s mission statement?.How will you cultivate that role in 2019?.Ayush Parashar: AI is at the heart of our company’s mission statement to make data discovery and working with data ubiquitous inside an enterprise.
It’s central to providing an easy user experience where different personas like a data analyst or a data engineer can work on data easily.
In 2019, we look forward to expanding the AI tools that substantially broaden intuitive and seamless ways to interact with data.
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