News Category: Media Coverage

News site Datanami recently covered dotData's launch of our new dotData Cloud, cloud-based, no-code AI Automation solution designed for BI teams and data engineers in organizations just getting started with AI. dotData announced dotData Cloud, an AI-automation software platform that helps businesses with small or no data science teams to get measurable business value in 45 days. DotData's new cloud-based offering offers access to their fully automated full-cycle data science automation platform, a core component of dotData's customer's business operations. Using dotData Cloud's automated machine learning capabilities, Sticky.io's data science and analytics team developed a model to better analyze customer data. Before dotData Cloud, it could take months to find viable models and move them to a new cluster. Among smaller to medium-sized companies, there is a need for full-cycle automation platforms. dotData Cloud is a cloud-based platform that helps companies build, deploy and scale AI models in a few…

dotData's CEO, Dr. Ryohei Fujimaki P.h.D., recently sat down with Enterprise AI to as part of a group to discuss what industries were likely to see the most growth in Enterprise AI adoption during 2021. Farabet, vice president of AI infrastructure for GPU and AI chipmaker Nvidia, said that AI in 2021 will take the role of a compiler, allowing more people to create AI programs. Neo4j, an artificial intelligence database vendor, expects faster innovation cycles to bring new techniques to enterprises. Alicia Frame said that data scientists would focus on solving big problems in 2021 and that graph algorithms and embeddings will become more mainstream. In 2021, Joanna Lowry-Duda believes the democratization of AI will be more prevalent, with more AI-based toolkits available for general consumption. In 2021, AI research will gain deeper insights that will profoundly shape industries, according to Lomax Ward, a co-founder with venture capital firm…

News site Database Trends and Applications recently covered dotData's launch of our new dotData Cloud, cloud-based, no-code AI Automation solution designed for BI teams and data engineers in organizations just getting started with AI. Read more at Database Trends today" Related Articles

In the news, dotData CEO Dr. Ryohei Fujimaki P.h.D. discusses how the continued adoption of AI and Machine Learning in the enterprise will make AI and MLOps skills top areas for organizations to focus on in 2021 and beyond. In 2021, more companies will be embracing digital transformation driven by the ongoing impact of the COVID-19 pandemic. IT leaders will focus on cybersecurity and cloud-based applications and move to work from home. In 2020, IT hiring will increase because technology professionals' unemployment rates are lower than the national average. Data scientists and Python developers are increasingly valuable in the next decade as companies look to refocus on data analytics and software building. Over the next 12 months, nuanced AI and machine learning roles, data experts, and process experts will be indispensable. LinkedIn's Top 15 Emerging Jobs in the U.S. include AI specialists with an annual hiring growth rate of 74…

In the news, dotData CEO Dr. Ryohei Fujimaki P.h.D. discusses how Automated Feature Engineering and the advent of new automation platforms will allow Business Intelligence Professionals to begin contributing to their organization's AI development efforts by creating predictive analytics dashboards quickly and easily. Modern BI tools present vast opportunities for organizations, allowing businesses to unearth new insights, efficiencies, and innovations and become more proactive in carrying out daily operations. 2.5 quintillion bytes of data are produced by people every day, and it will grow faster each year. Business intelligence leaders are already struggling to translate this explosion of complex data into actionable insights. As a result, there will be a significant demand for more advanced, easy-to-use data translation tools. Accelerated data movement to the cloud will disrupt existing BI infrastructure. "On-premises" data stores fail to scale compared to the explosive growth in data assets. As a result, more and more…

With every new technology, especially in the early days, comes a share of misconceptions, fallacy, and ambiguity. That’s why our CEO Ryohei Fujimaki shares the top five myths and reality of AutoML with RTInsights. Businesses are looking for ways to make AI more accessible by automating complex manual data science processes. Data scientists are typically hired by Fortune 500 companies and work for a handful of companies. Most companies struggle to find and retain data scientists. Businesses can empower citizen data scientists to make data-driven decisions using advanced analytical tools that automate complex manual data-science processes. The focus of first-generation AutoML platforms has been on building and validating models automatically, but next-generation AutoML platforms can automate the entire data science process, including feature engineering and data preparation, to build models in days instead of months. Feature engineering entails building features using relational, transactional, temporal, geo-locational, or text data to test…

Our CEO, Ryohei Fujimaki, PhD discusses investing in #AI during the economic downturn for @thenextweb #DataScience #ArtificialIntelligence As of April 28, there have been nearly 198,000 confirmed deaths and more than 2.8 million confirmed cases across the globe. California, New York, and Philadelphia had to "shelter in place" and leave their homes only for necessities. Even harsh measures will take months for families and communities to heal from the COVID-19 pandemic. The COVID-19 outbreak has affected the world's economy by causing an economic contraction in China and a decrease in the US stock index. Planning for an economic downturn is a complex proposition, especially when the downturn is prolonged and severe. According to Gartner, 14% of organizations have already adopted AI, while 48% plan to adopt the technology by 2020. Using AI/ML-based predictions and group contracts, a large multinational bank doubled the close rate for new clients by deploying AI/ML-based predictions.…

What impact will #COVID19 have on #enterprise investments in #ArtificialIntelligence? Our CEO Ryohei Fujimaki recently shared his thoughts with @TechTarget's @markrlabbe #AI #DataScience #DigitalTransformation Enterprises have seen their budget extras wiped out by the virus pandemic, but they should not give up on digital transformation projects. AI is important for enterprises because companies generate vast amounts of data, which AI can't process. Businesses will need to turn to AI at some point, but they should augment their existing people with automation for the short term. Automation tools can automate repetitive tasks quickly. The lack of budget makes it hard to invest in AI and digital transformation projects. Enterprises can augment their workers with tools rather than replace entire systems, making more accessible and cheaper AI digital transformation. New and old customers should focus on smaller, necessary projects instead of large, big bang digital transformation projects. As the world starts moving…

Published @SME_MFG -- With #AutoML 2.0, firms can leverage the wealth of data at a manufacturer’s disposal, to create ML/AI algorithms in a matter of days," our CEO Ryohei Fujimaki shared his insights with SME for this article.  #manufacturing #MachineLearning #DataScience #ArtificialIntelligence #ML and AI Manufacturers must manage sensor performance and forecast supply chain issues and inventory. To create an effective AI algorithm for predicting equipment failures, we need to leverage existing sensor data as well as the skills of data scientists. AutoML 2.0 provides a solution to the manual development of AI/ML algorithms. Finding the right AutoML solution can be tricky. Manufacturers should focus on the product's automation for feature engineering and how it can help them speed up their development lifecycle. Using API-based delivery of ML algorithms developed with AutoML systems makes retraining the algorithm easy but introduces too much latency for intelligent manufacturing operations. Manufacturers can deploy…

Our CEO Ryohei Fujimaki, Ph.D. discusses the role #AI plays in adapting the new normal of a 90% economy via @datanami https://bit.ly/2SGlMet Consumers flocked to Amazon.com and other e-commerce sites during COVID-19, and that's changed the way machine learning-based forecasting methods work for companies in the retail and consumer goods sectors. In May, companies will be looking to data and AI to guide their decision-making as businesses re-open. Retail and consumer goods are experiencing significant volatility, which is causing the industry to change a lot. The U.S. economy is doing better than China, but it is far from ordinary. The Economist says the 90% economy is one of financial hardship and fear of the COVID-19 lockdown. Consumers will avoid large crowds, ridesharing, and public transportation, and trade shows this year. They will also shop locally. A pandemic of COVID-19 will alter consumer behavior. Some businesses will fail in the next several months.…

#ICYMI: Last week, we unveiled our new all-inclusive bundle of #technology and services, dotData AI-FastStart, which is designed to empower #BusinessIntelligence teams. Learn more via @RTInsights: https://bit.ly/3c3twyC #AI #DataScience #MachineLearning

Ryohei Fujimaki, PhD | dotData CEO - discusses five key factors why white-box data science models are superior to black-box models for deriving business value from data science. Data science will help organizations transform from data to knowledge, driving performance and competitive advantage. Data science platforms and methodologies should use the white-box model approach or the black-box model approach. The industry standard for machine-learning projects, black-box testing, is often a lack of actionable insights, leading to a lack of accountability. In this article, dotData founder, CEO, and Ph.D. Ryohei Fujimaki discusses the key factors why white-box models are superior to black-box models for data science projects. There are two types of machine learning models: linear and nonlinear models. Non-linear models (black-box models) are opaque while white-box models are transparent. Data scientists create complex features and black boxes; they create nonlinear transformations, and deep learning (neural networks) computationally generates features; this…