In order to appreciate the great success of NASA and humanity, we are again exploring the topic, but with a more in-depth perspective. As an AI Research Scientist at NASA Jet Propulsion Laboratory, Shreyansh introduced us to the evolution of autonomous robots for space exploration with AI and ML, challenges and solutions to training deep learning models in the Martian environment, and lessons and future AI-powered exploration.
They say great ideas are born out of necessity. The data & analytics field is no exception. Although DataOps is seen as a fairly new and disruptive methodology, leaders in data and AI pioneered in finding more efficient, agile and easier ways of designing data pipelines long before it was incarnated into a defined concept.
Data loss prevention (DLP) may be an unfamiliar term, even to business leaders working hard to follow cybersecurity best practices. In this blog post, we’re giving you a primer on why DLP is a massive undertaking for even the best IT teams, and why an automated solution is the best way to protect your business-critical data.
Initially slower to catch up with the data-driven transformation, the heavy-asset industry has certainly accelerated the adoption of predictive analytics systems that provide multiple maintenance advantages such as fewer outages and breakdowns, extended lifecycle of the assets and equipment, better plant safety, and enhance the overall quality of operations and supply chain processes.
Artis Taylor, Manager IT at FedEx Logistics will be presenting on this topic in his presentation on Enabling DataOps in the Cloud at the Data Innovation Summit, where he will elaborate on DataOps implementation as the journey to the cloud. We talked to Artis to get more information before his session.
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