This article is part of our series that explores the business of artificial intelligence. The growing digitization of nearly every aspect of our world and lives has created immense opportunities for the productive application of machine learning and data science. Organizations and institutions across the board are feeling the need to innovate and reinvent themselves by using artificial intelligence and putting their data to good use. And according to several surveys, data science is among the fastest-growing in-demand skills in different sectors. However, the growing demand for AI is hampered by the very low supply of data scientists and machine learning experts.

The growing digitization of nearly every aspect of our world and lives has created immense opportunities for the productive application of machine learning and data science. Organizations and institutions across the board are feeling the need to innovate and reinvent themselves by using artificial intelligence and putting their data to good use. And according to several surveys, data science is among the fastest-growing in-demand skills in different sectors. However, the growing demand for AI is hampered by the very low supply of data scientists and machine learning experts. Among the efforts to address this talent gap is the fast-evolving field of no-code AI, tools that make the creation and deployment of ML models accessible to organizations that don't have enough highly skilled data scientists and machine learning engineers. In an interview with TechTalks, Nenshad Bardoliwalla, chief product officer at DataRobot, discussed the challenges of meeting the needs of machine learning and data science in different sectors and how no-code platforms are helping democratize artificial intelligence.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence (AI) is becoming increasingly mainstream – very quickly, and across a multitude of areas and applications. According to Jack Vernon, senior research analyst, European AI Systems, for IDC, 69% of organizations are either using AI already or plan to in the next 24 months. "But even more," Vernon said, "AI has clearly moved from the experimental phase to mission critical, with businesses realizing real value, from improved growth and revenue, to cost reduction, to operational efficiency. Now more than ever, businesses need an AI platform that is adaptive, shifting and adjusting to even the most unpredictable market conditions."

The growing digitization of nearly every aspect of our world and lives has created immense opportunities for the productive application of machine learning and data science. Organizations and institutions across the board are feeling the need to innovate and reinvent themselves by using artificial intelligence and putting their data to good use. And according to several surveys, data science is among the fastest-growing in-demand skills in different sectors. However, the growing demand for AI is hampered by the very low supply of data scientists and machine learning experts. Among the efforts to address this talent gap is the fast-evolving field of no-code AI, tools that make the creation and deployment of ML models accessible to organizations that don't have enough highly skilled data scientists and machine learning engineers.

Black box "magic" isn't acceptable now that companies are moving from the "fun" phase of playing with AI's capabilities to implementing the technology in essential business workflows. "Every company in the world is looking for the opportunity to take advantage of AI to improve their business processes, whether it's to improve their revenue, lower their cost profile, or lower their risk," said Nenshad Bardoliwalla (pictured), chief product officer at DataRobot Inc. They discussed DataRobot's AI Cloud and self-service AI. (* Disclosure below.) The danger of AI is that algorithms aren't infallible. Trust is provided through transparency and explainability, which together allow data scientists to see into the workings of the AI's "mind" to understand how it arrived at insights.

DataRobot today announced DataRobot Core, an offering that broadens the company's AI development tool portfolio with new resources, functionality, and community features. The launch of Core coincides with the release of the latest version of DataRobot's enterprise platform, which the company says introduces "broader and more sophisticated" analytical capabilities as well as new ways to manage AI systems in production. The demand for AI, combined with the complexity in achieving deployment, has created delays in data science initiatives for businesses. While Harvard Business Review reports that 52% of companies accelerated their AI adoption plans because of the COVID-19 crisis, many expect that challenges will impede progress. According to a recent Teradata survey, businesses see a lack of IT infrastructure and access to talent as major blockers, followed by lack of budget for implementation, complications around policies, regulations and rights, and impact on customer expectations.

Paxata today announced it has raised 33.5 million to bolster the machine learning and semantic analysis foundations of its enterprise information platform. Led by Intel Capital, today's investment is Paxata's fourth funding round and brings its total capital raised to 61.5 million. Paxata's platform provides a self-service, visual approach for information management to large corporations. "We target the one billion people in the enterprise who know how to use Microsoft Excel and want to use data to make business decisions," said Nenshad Bardoliwalla, cofounder and chief product officer of Paxata, at the Intel Capital Global Summit. According to Bardoliwalla, Paxata helps those who need fast access to meaningful data without having to write code or learn to use Hadoop. Called AnswerSets, the company's product connects big data analytics to the operational applications used by business analysts, chief data officers, and IT managers.