
Eazy Ml
Information Technology And Services, 4400 Us 9, New York, United States, 51-200 Employees
Phone Number: +18*********
Who is EAZYML
Founded by Bell Labs research veterans, and associated with breakthrough startups like Amelia, EazyML, specializes in Transparent Machine Learning.. Early on EazyML founders saw the need ...
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Headquarters: 4400 Us 9, New York, United States
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Date Founded: 2013
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Employees: 51-200
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Revenue: $1 Million to $5 Million
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Active Tech Stack: See technologies
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CEO: Deepak Dube
Industry: Information Technology and Services
SIC Code: 7372
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NAICS Code: 513210 |
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EazyML Org Chart and Mapping
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Frequently Asked Questions Regarding EazyML
Answer: EazyML's headquarters are located at 4400 Us 9, New York, United States
Answer: EazyML's phone number is +18*********
Answer: EazyML's official website is https://datanomers.com
Answer: EazyML's revenue is $1 Million to $5 Million
Answer: EazyML's SIC: 7372
Answer: EazyML's NAICS: 513210
Answer: EazyML has 51-200 employees
Answer: EazyML is in Information Technology and Services
Answer: EazyML contact info: Phone number: +18********* Website: https://datanomers.com
Answer: Founded by Bell Labs research veterans, and associated with breakthrough startups like Amelia, EazyML, specializes in Transparent Machine Learning.. Early on EazyML founders saw the need for Transparency in AI/ML, and their subsequent research led to the widely acclaimed transparent ML platform, EazyML. Decision-AI digitizes your business process not only enhances productivity by automation but equally importantly, helps make better decisions for increased profitability. The challenge though, especially for regulated sectors, is how do you ensure its compliance and report it for audit? Transparency in AI/ML is a must. EazyMLs Transparent/Responsible AI provides the solution for proactive compliance, differentiating from competing ML majors by making Transparency Actionable (suggest a way forward to remedy). As AI/ML becomes integral to your enterprise, further challenges arise: What if the dynamic business environment causes the model to become stale and misfire? How do you detect it in good time before it adversely impacts your business? What if the experts for a business process disagree with your models prediction? How does your model reconcile the difference to earn their trust? Sustainable automation is possible by EazyMLs Transparent AI/ML. Do a PoC to evaluate, and if you find it helps your business, make the AI/ML easily align with your process for quick benefits while alleviating risks.
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