Machine Learning as a Service - MLaaS
Vinod Sharma
Chief Technology Officer | Artificial Intelligence (AI, ML & DL) | Strategic Partnerships | Fintech | Security & Risk
This post was originally published at Myblog first on Sept-10-2017. To find out more about me click here.
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MLaaS is neither new nor rocket science or an unknown service. In today’s time there are hundreds of companies in this domain which are working as a service provider of #MLaaS (SPMLaaS). Machine learning is into so many services and applications as on date and we may not even aware of them or most of them. In the area of FinTech, Medical, Law and almost every service which needs/has repeated actions/steps every time has made use of it as a service knowingly or unknowingly. Feature engineering as an essential to applied #machinelearning. Using domain knowledge to strengthen your predictive model or prescriptive model out of prediction can be both difficult and expensive. To help fill the information gap on feature engineering, MLaaS hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena....To Read full post click here...
When I came across the assertion that each bootstrap sample always contain on average approximately 2//3 of the observations. I did not know the secrete and never understood that the chance of not being selected in any of draws (say n) from samples (say n) with replacement as . I never know that each bootstrap sample or bagged tree will contain on average approximately 2//3 of the observations. What if this was built and given as part of some library in a package to justify my argument which is Machine Learning as a Service(Please comment if you know this), then this would have been much easier. Super swift help and understanding on how unsupervised feature learning works in the case of deep learning for images.....To Read full post click here...
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