dotData Releases New White Paper on Why Machine Learning Automation Falls Short of Enterprise Needs
Paper highlights the benefits of automating the full data science process, with focus on automated feature engineering
Cupertino, CA – December 11, 2018 – dotData, the first and only company focused on delivering end-to-end data science automation and operationalization for the enterprise, today released a new white paper, “Why Machine Learning Automation Alone Is Not Enough,” that discusses the distinctions between data science automation and machine learning (ML) automation, and the benefits to the enterprise of automating the full data science process. The white paper is available for download on dotdata.com.
“While machine learning automation has been highly publicized and has helped progress some ML initiatives, machine learning automation platforms only automate a part of the data science process. They rely on the manual efforts of domain experts and data scientists to complete critical steps such as data preparation and feature engineering,” said Ryohei Fujimaki, PhD, CEO of dotData. “As a result, data science is still extremely manual and not yet fully democratized.”
The white paper details one of the core components of data science automation – feature engineering. Feature engineering is often the most challenging and time-consuming step in a data science project. It is based on both intuition as well as technical acumen, making it highly dependent on the skill sets and experience level of the domain experts and data scientists involved.
“Enterprises are challenged to scale their machine learning projects, and are often held back by the lack of available talent, time and financial resources,” said Fujimaki. “Our approach to automating the entire process enables all data science team members, regardless of skill, to contribute, enabling greater throughput and deployment of production-ready models.”
dotData’s AI-powered Data Science Automation Platform completely automates the entire data science process, from data collection through production-ready models, including feature engineering. As a result, the entire data science process is accelerated from months to days, enabling companies to rapidly scale their AI/ML initiatives to drive transformative business changes.
The dotData Platform also democratizes the data science process by enabling more participants with different skill levels to effectively execute on projects, making it possible for enterprises to operationalize 10x more projects with transparent and actionable outcomes.
dotData is the first and only company focused on delivering end-to-end data science automation for the enterprise. dotData’s AI-powered data science automation platform speeds time to value by accelerating, democratizing and operationalizing the entire data science process, from raw data through feature engineering to ML models in production. dotData is delivering new levels of speed, scale and value in successful deployments across multiple industries, including several Fortune Global 250 clients. For more information, visit dotdata.com.