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Empowering Analysts to use machine learning & AI to solve business problems
As Product Managers, Business Analysts, Marketing Analysts, Merchandising Analysts, Production Analysts, etc., the advent of AI has catalyzed the generation of a large volume of ideas that could deliver business impact. If there is unbounded supply of Data Scientists in the Company, we would assign such high-cost resources to every idea, and assess which ones will work. Unfortunately, this is wishful thinking. So, Product Managers and Analysts need no-code platforms that will help them explore the historical data they have, apply/test their business hypotheses and quickly build, deploy and test ML models easily. While these are no-code, an ideal platform for this purpose will produce production-quality Python code for the ideas that have production merit, and give Data Scientists a head-start.
Ideally, these no-code approaches should be available from tools of their business -- Excel, PowerBI, Tableau, Salesforce, Dynamics, custom business applications -- essentially bringing AI to where they spend most of their time, rather than most of the tools available in the market which require them to go to custom AI tools. Analysts have gone from IT providing BI to self-service BI. With AI, why should they have to take a step back and go to IT providing AI? Why not offer them self-service AI too! Also, AI tools are too expensive compared to BI tools; it is time to ask Why?
It is time to rethink how business people access and leverage AI/ML. Business Scientists and Citizen AI developers need better tools. It is time to demand products that are easy to use and priced right for self service!
Numtra suite consists of Numtra For Web, Numtra for Excel, Numtra for PowerBI, and Numtra PaaS.
Numtra For Excel - Model Training (Auto-ML)
Numtra For PowerBI - Model Training (Auto-ML)
Numtra For Web - Model Training (Auto-ML)
Numtra For Web - Feature Engineering (Manual)
Numtra For Web - Model Training (Manual)
Numtra Product Brochure (PDF File)
Quick-Start Guide to Machine Learning (PDF File)