Machine Learning as a Service – MLaaS

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 Machine Learning. 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.

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.

If you understand basic machine learning concepts like supervised and unsupervised learning, you should feel ready to get started. With MLaaS as that will not only allow you to perform your task but will also give you the chance to learn how to implement feature engineering in a systematic and principled way. MLaaS can help to practice better data science for any one. As some one said bias variance tradeoff & debugging models can be a very useful learning curve and art of figuring out if you need more instances or more dimensions for your model. Same way MLaaS can be free gift to all new comers and can provide foundation for every system to solve, learn and work. Dont worry AI OS are not far which will be the best combination of OS based on AI and MLaaS built in on top of MLaaP (Machine Learning as Platform).

Explaining or gaining common practices and mathematical principles to help engineer features for new data and tasks. Personal biometric data i.e measurements of  heart rate levels of blood sugar, blood pressure, etc. What we call “data” are observations of real world phenomena. For instance, stock market data might involve observations of daily stock prices. MLaaS helps and  features; “feature engineering”  as it’s very important and integral part to tell us how to do well. MLaaS allowing use of  common methods for different data types, including images, text, and logs. Also help is understanding how different techniques such as feature scaling and principal component analysis work can support. MLaaS is coming to get its place in our day to day work life without without choice.