EazyML's Augmented Intelligence mines insights from data and associates a (patent-pending) confidence score with it. It uses this score to define data SLA for how good is it
Is the data available good to do an analysis? Or do we have to spend considerable time cleaning it up before we can mine it for insights? Based on experience and testing, the experts have warm fuzzies about it; EazyML’s Augmented Intelligence answers this with precision – with data SLA (confidence score):
Even if you’re doubtful about data, execute it using Augmented Intelligence to find out how good is it: 0.0 (poor), 1.0 (excellent), or more likely somewhere in between.
AI/ML is key to digitization effort in the industry worldwide - no sector can afford the business disruption as happened due to the pandemic. What has stymied the AI/ML drive is its lack of transparency - the data is opaque (don't know how good is it) as is the model (doesn't explain itself).
EazyML, with its patent-pending, excels at Transparent AI/ML - in particular, it uses Augmented Intelligence to answer the all-important question: Is your data good enough to undertake the expensive AI/ML exercise, saving precious time, resources and money by knowing apriori if your AI/ML project will succeed or not:
https://www.forbes.com/sites/forbestechcouncil/2021/10/27/get-your-data-right-prior-to-training-ml/?sh=482ae6217230 answers a key question “Is the data biased/incomplete” before modeling (garbage in, garbage out)
1) Transparency of ML ensures regulatory compliance, and importantly, builds trust between the expert and the intelligent machine, its automation - ready acceptance of the aolution
2) Transparent ML mines data for insights, expresses them as simple rules to tune your policies/rules for various business functions for better performance. Just imagine the tremendous ROI if the performance of the various functions improve by a couple of % points - data-assisted decision-making