dotData Announces Free Trial of dotData Enterprise
SAN MATEO, Calif., April 1, 2020 – dotData, focused on delivering full-cycle data science automation and operationalization for the enterprise, today announced that its autoML 2.0 platform, dotData Enterprise, is now available for proof of concept testing on a free trial basis, enabling users to preview how dotData Enterprise can help accelerate their AI and machine learning (ML) initiatives, and derive more business value from their data.
The dotData trial program enables companies to build a viable, usable AI/ML model from their own data set, regardless of data format. Leveraging intelligent data management and preparation features, dotData Enterprise can connect to both flat-files as well as relational data, and automatically generate the required schemas.
Each dotData Enterprise trial will be fully-guided and supported by the dotData data science team, who will assist each company in building a use-case, understanding their data and optimizing the trial experience. dotData’s data science team will also guide the client through AI model selection and interpretation of the results. dotData can host the software environment if needed, or can utilize the client’s own AWS or Azure environment.
“We are seeing a rapid demand for data science and AI capabilities among enterprises of all sizes, many of which do not have the resources to build and deploy their own data science program,” said Ryohei Fujimaki, founder and CEO of dotData. “Our trial program will enable customers to perform an actual proof of concept trial with their own data, to demonstrate how quickly and easily it is to deploy dotData Enterprise to accelerate their data science initiatives.”
dotData provides AutoML 2.0 solutions that help accelerate the process of developing AI and Machine Learning models for use in advanced predictive analytics BI dashboards and applications. dotData makes it easy for BI developers and data engineers to develop AI/ML models in just days by automating the full life-cycle of the data science process, from business raw data through feature engineering to implementation of ML in production utilizing its proprietary AI technologies. dotData’s AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination, and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects that are fundamental to developing predictive analytics solutions.
dotData democratizes data science by enabling BI developers and data engineers to make enterprise data science scalable and sustainable. dotData automates up to 100 percent of the AI/ML development workflow, enabling users to connect directly to their enterprise data sources to discover and evaluate millions of features from complex table structures and huge data sets with minimal user input. dotData is also designed to operationalize AI/ML models by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflows. This can further automate the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, AI/ML development becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.
For more information or a demo of dotData’s AI-powered full-cycle data science automation platform, please visit dotData.com.
dotData Pioneered AutoML 2.0 to help business intelligence professionals add AI/ML models to their BI stacks and predictive analytics applications quickly and easily. Fortune 500 organizations around the world use dotData to accelerate their ML and AI development to drive higher business value. dotData’s automated data science platform accelerates ROI and lowers the total cost of model development by automating the entire data science process that is at the heart of AI/ML. dotData ingests raw business data and uses an AI-based engine to automatically discover meaningful patterns and build ML-ready feature tables from relational, transactional, temporal, geo-locational, and text data.