DotData today announced version 2.0 of its artificial intelligence and machine learning platform for enterprises. The company automates data science so it can accelerate the adoption of AI and machine learning in corporations. DotData CEO Ryohei Fujimaki said in a fireside chat with me at our Transform 2020 event that enterprises can implement AI and ML tools that generate better business insights and money-saving results. "Everyone is under high pressure to deliver more results with less resources to survive in this economic downturn," Fujimaki said. "AI automation will change this game. It significantly accelerates the turnaround from months to days."
Machine learning may seem like a mysterious creation to the average consumer, but the truth is we're surrounded by it every day. ML algorithms power search results, monitor medical data, and impact our admission to schools, jobs, and even jail. Despite our proximity to machine learning algorithms, explaining how they work can be a difficult task, even for the experts who designed them. In the early days of machine learning, algorithms were relatively straightforward, if not always as accurate as we'd like them to be. As research into machine learning progressed over the decades, the accuracy increased, and so did the complexity.
Founder and CEO of DotData, Ryohei Fujimaki, explains how automation can help the data science industry become more efficient. Of the many technologies that will shape how we work in the future, automation is one of the most hotly debated. Some look forward to the new avenues it will open up while others fear it will make their skills redundant. Dr Ryohei Fujimaki, founder and CEO of data science company DotData, believes that data scientists are among those that will benefit the most. Fujimaki's team at DotData is helping companies accelerate their data science process.
The notion of using data to predict future outcomes is far from new. Even highly technical products that performed "predictive analytics" analysis have already been available to enterprise organizations for many years. The notion of developing and deploying custom-built predictive solutions, however, have, for the most part, been the exclusive domain of Fortune 500 companies. The rarity of predictive analytics in the enterprise is mostly due to the technical complexity needed to create, train, and deploy the complex AI and Machine Learning (ML) models required to successfully develop predictive solutions. Over the past few years, the world of AI and ML development has seen rapid change.
A data science spinoff from NEC Corp. has raised additional early stage funding to accelerate development of its automated machine learning platform. DotData, San Mateo, Calif, was spun off from its Japanese parent company last year. The new company announced this week it raised $23 million in Series A funding 18 months after its launch and seed funding round. To date, DotData has raised $43 million. The latest funding announced on Wednesday (Oct.