News Category: Media Coverage

Choosing data science software for your organization can be a daunting task. In this article from BigData Review, our CEO Ryohei Fujimaki gives his take on selecting data science software. Before selecting a data science platform, the stakeholders should determine the best uses cases, requirements, and impact, keeping in mind the primary users of your data science application and the programming language that they use. The rationale for selecting a particular Data Science platform depends on the target user's preference for customization, flexibility, and automatic discovery of new features. A no-code or low-code approach to data science is an essential consideration in selecting a data science platform. A no-code environment is ideal for BI and analytics teams, which prefer visual tools that leverage drag-and-drop functionality to make the data science process easier for non-data scientists. Head over to the article to read more. @BigData_Review #machinelearning

The global life insurance and retirement industries face an inflection point due to the convergence of challenging economic, technological, competitive, and societal headwinds. Going digital has been a top priority, as it helps reduce cost and enhances customer experiences. Digital transformation has led to the increasing adoption of predictive analytics, artificial intelligence, and automation in various business functions. According to McKinsey estimates, the potential total value of AI and analytics across the insurance vertical is approximately $1. Soon, AI will be deeply embedded into the insurance value chain, providing unmatched power to insurers: Automating manual processes in underwriting. Eliminating errors and inefficiencies in claims processing. Enabling predictive insights to deliver superior outcomes. Our CEO, Dr. Ryohei Fujimaki, Ph.D., discusses the top challenges that AI and machine learning will help solve in the insurance industry, including: Underwriting and pricing: The underwriter needs much information for commercial property insurance, such as occupancy, data…

AI has a 1% problem: only the most prominent tech firms, the Facebooks, Amazons, Apples, Netflixes, and Googles, or FAANGs, of the world, have the resources required to pull it off. Thanks to the rapid advance of data science technology, the democratization of computing in the cloud, and data availability, the 1% problem is starting to fade. We are amid great democratization of big data and AI, benefiting companies of all sizes and maturity levels. Deloitte Insights proclaimed that "we are entering a new chapter in the adoption of the current generation of AI technologies. Capabilities are advancing, it is becoming easier to develop and implement AI applications, and companies are seeing tangible benefits from adoption." Not everyone has adopted AI technologies since there are still barriers, and many are working to scale the benefits. AI's 'early adopter' phase, however, seems to be ending; the market is now moving into…

Our CEO Ryohei Fujimaki spoke with @VentureBeat to discuss dotData’s broader approach to simplifying #AI workloads for anyone with more data than time. Many AI experts say that running the AI algorithm is only part of the job. Preparing the data and cleaning it is a start, but the real challenge is to figure out what to study and where to look for the answer. Finding the right features for the AI algorithm to examine often requires a deep knowledge of the business itself for the AI algorithms to be guided to look in the right place.dotData wants to automate that work. The company wants to help the enterprises flag the best features for AI processing and find the best place to look for such features. Join dotData CEO, Dr. Ryohei Fujimaki, P.h.D, who joined the folks at VentureBeat to discuss how dotData can empower a broad swath of new AI…

InsideBIGDATA recently covered the launch of dotData Py Lite. dotData Py Lite enables data scientists to install and execute AI automation in one minute, develop AI models in ten minutes, and deploy AI on their desktop in seconds. The dotData Py Lite platform supports automated feature engineering and AutoML and supports cluster-based deployment for scale-out. Feature engineering is critical to developing accurate predictions, and dotData Py Lite can create features easily. dotData Py Lite provides a powerful yet easy-to-use environment to explore and evaluate hypotheses for AI and ML via automated feature engineering. At the Gartner Data & Analytics Summit, dotData will showcase Py Lite, and Dr. Fijumaki will discuss how automation improves AI models. dotData automates feature engineering, automates the discovery of hidden patterns in complex tables, and builds greater models for AI and ML. Experienced data scientists can use dotData's AutoFE to augment in-house developed features and increase…

AIThority recently covered the launch of dotData Py Lite. dotData Py Lite enables data scientists to install and execute AI automation in one minute, develop AI models in ten minutes, and deploy AI on their desktop in seconds. The dotData Py Lite platform supports automated feature engineering and AutoML and supports cluster-based deployment for scale-out. dotData Py Lite provides a powerful yet easy-to-use environment to explore and evaluate hypotheses for AI and ML via automated feature engineering. dotData automates feature engineering, automates the discovery of hidden patterns in complex tables, and builds greater models for AI and ML. Experienced data scientists can use dotData's AutoFE to augment in-house developed features and increase the accuracy of AI and ML models. DotData's no-code AI/ML solution allows users to build production-ready features and machine learning models from raw business data. Read more about dotData Py Lite at AIThority, then come back to learn…

dotData's Sachin Andhare was recently published in industry publication SmartIndustry on the subject of applying machine learning to operational data. As sensors everywhere generate time-series data, manufacturing is becoming driven by advanced analytics and artificial intelligence to improve production, streamline processes, and make better decisions. AI allows manufacturing companies to drive innovation. Augmented data discovery, predictive analytics, and machine learning-based modeling are required to drive AI innovation and adoption in manufacturing. While AI can bring dramatic benefits to manufacturing, implementing it is challenging because of the necessary steps and data science skills needed. Industrial data management requires a scalable environment with high-performance analytics is needed. Using AI automation, manufacturers can reduce the cost to implement AI and speed up the painfully slow AI deployment. The ideal AI-automation solution for the industrial manufacturing industry should automatically use an AI-based engine to discover and build ML-ready feature tables from operational data. Augmented-data…

Database Trends and Applications recently covered the launch of dotData Py Lite - the first AI Automation Solution designed to live on a data scientist's laptop. You can read more about it at Database Trends, then come back to learn about dotData Py Lite.

AI TechPark recently covered the dotData Py Lite launch. dotData Py Lite enables data scientists to deploy dotData on their desktop in a short amount of time, without having to rely on large and expensive enterprise-AI environments. Features and benefits of dotData Py Lite include automated feature engineering, AutoML, and predictive analytics. Feature engineering is critical to developing accurate predictions, and features can be developed quickly using the dotData Py Lite tool. dotData Py Lite is a Python library for performing AI and ML experiments for data scientists, data engineers, and IT and engineering teams. dotData automates feature engineering, automates the discovery of hidden patterns in complex tables, and builds better models for AI and ML. Experienced data science teams can use Automated Feature Engineering (AutoFE) to prototype AI and ML models quickly. Read more about dotData Py Lite at AITechPark, then come back to learn about dotData Py Lite.

TDWI recently covered our launch of dotData Py Lite. dotData Py Lite is designed for data scientists to deploy AI models to cloud containers. The dotData Py Lite containerized AI automation solution enables data scientists to test hypotheses and refine machine learning models. dotData Py Lite supports automated feature engineering, AutoML, and data analysis and automates feature engineering, a manual and time-consuming step, from data and feature engineering to ML scoring and AI features for AI and ML algorithms. Experienced data science teams can use dotData's AI features to augment in-house developed features, and the company's automated machine learning solution can be used for reporting and dashboards. You can read more about it at TDWI, then come back to learn about dotData Py Lite.

Enterprise AI recently covered dotData's launch of dotData Py Lite. With dotData Py Lite, python data scientists can deploy dotData on their desktops, making it easy for them to explore 100x more features with dotData's award-winning automation. dotData Py Lite is an automated feature engineering and ML scoring tool for data. The secret to great machine learning algorithms is feature engineering. DotData Py Lite makes it easy to build robust features. dotData Py Lite provides a powerful yet easy-to-use environment to explore and evaluate hypotheses for AI and ML via automated feature engineering. dotData automates feature engineering, automates the discovery of hidden patterns in complex tables, and builds greater models for AI and ML. dotData has been recognized by Forrester, the AI breakthrough awards, CRN, and CB Insights as a leader in the 2019 New Wave for AutoML platforms. You can read more about it at EnterpriseAI, then come back…

Datanami recently covered the launch of dotData Py Lite - the first AI Automation Solution designed to live on a data scientist's laptop. You can read more about it at Datanami, then come back to learn about dotData Py Lite.