The world of enterprise data applications such as Business Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI) is becoming increasingly critical for organizations of all sizes. As these technologies advance, businesses face the challenge of choosing the best tools to apply in different situations. The landscape of BI, ML, and AI tools has become commoditized and fragmented, leading to the development of various tools for specific purposes. Despite this, data application development in enterprises often remains siloed, and the full potential of data is not extracted. Enter Feature Factory – a new paradigm that aims to revolutionize open and scalable enterprise BI, ML, and AI development. A Commoditized and Fragmented Landscape The Enterprise ML and AI tools market, as well as the BI tools market, is highly commoditized, with a wide variety of tools available for use, each with its strengths and weaknesses. The tools range from open-source libraries…
This article was originally posted February 18, 2020 on Forbes Cognitive World - AI Contributor Group. dotData's Founder and CEO - Ryohei Fujimaki, PhD was an interviewed contributor for this important information share. In today's competitive global insurance market, insurers are striving to create new ways to successfully overcome two important and opposing forces: creating short-term revenue growth for the company, while also meeting customers' needs for product offerings and services that are personalized, relevant, and provide long-term value. In meeting these challenges, insurers realize the strategic importance of their data, and how AI and machine learning (ML) can help them better achieve their business goals. But while investments in AI are growing, challenges in resources, technology infrastructure, and the ability to operationalize models quickly and efficiently can prevent insurers from fully leveraging AI and data science to drive business impact. These were some of the challenges faced by leading global…
As originally seen on Forbes Cognitive World, our CEO - Ryohei Fujimaki PhD was a primary contributor to this article. In case you've missed it, we've reposted the Original Article below. It's an AutoML WorldThe world of AutoML has been proliferating over the past few years - and with a recession looming, the notion of automating the development of AI and Machine Learning is bound to become even more appealing. New platforms are available with increased capabilities and more automation. The advent of AI-powered Feature Engineering - which allows users to discover and create features for data science processing automatically - is enabling a whole new approach to data science that, seemingly, threatens the role of the data scientist. Should data scientists be concerned about these developments? What is the role of the data scientist in an automated process? How do organizations evolve because of this newfound automation?AutoML 2.0, More Automation for…
Over the past several weeks, we have all seen the significant impact the Coronavirus (COVID-19) has had on public health, our societal norms and operations as well as how we conduct business. As the situation continues to evolve, we felt it was essential to connect and share the steps we are taking to provide a safe and healthy environment for our employees and to ensure business continuity, service, and support to our customers.Here is an overview of our current policies and actions: Per recent recommendations by local governments in both our Japan offices as well as our US headquarters, dotData employees are now working remotely to maintain social distancing and reduce the chance of inter-office contamination.We have taken great lengths to ensure that your sales manager, data science team, and support staff continue to be available, despite the challenges of the external market situation. As always, help is available directly…
Previously Published by: Forbes Cognitive World | Original Post: AI In Financial Services: Is Finance Ready For AutoML AI In Financial Services: Is Finance Ready For AutoML This past month I attended the AI for Finance show in New York where I was the host for a one-hour "tech talk" on the subject of AutoML in the world of financial services. With over 150 people in attendance, the audience was able to provide some pointed input on the topic of AI in Financial Services, and especially around the need for AutoML solutions in the space. The results were both surprising and exciting. A diverse audience Let's start with who was in attendance. While this may not have been a vast audience, the 150+ people that attended our presentation represented a relatively broad cross-sampling including many US top-tier financial services organizations - both from a technical and a business perspective - with…
