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.
Clearly MLaaS platform should if not already covers use cases like data modeling & data preprocessing as this task is most time, attention and focus consuming and one small mistake is enough to ruin the fun. This will help and provide better productivity. Experimentation is the another task which can come as use case due to the nature of machine learning which its all about learning and experimenting. Getting predefined templates and dashboards for our work model and required intelligence like payment intelligence, info-security intelligence, potential spending and earning intelligence etc. Where flexibility of choosing diffrent dashboards with diffrent themes, look & feel and the freedom in choosing the algorithm as all algorithms are diffrent and can provide diffrent results on same data.
Feature engineering as we spoke about earlier here. This engineering take up most of the time & cost for developing algorithms, applications and models, most of the time are common so why not have off the shelf arrangement. i.e REST interface as stand alone application. MLaaS expected to provide readily available data set for specific domains. with highly tuned components of solving specific problems in required area of expertise in any particular domain. This can be a perfect convergence of ability and demand and remember we are in the era of cheap computing, memory and processing power so another reason for MLaaS to come from clouds.
Now since we have talked applications, api’s, off the shelf arrangement, dashboards, cheap or low value hardware, clouds which clearly hint us that open source are democratising our data which any ways companies don’t manage (most of the companies not all the companies) as they manage their money. I red this joke on internet “no data in, no science out” but unfortunately its the truth of todays time. Machine learning and predictive techniques impact every major industry. It may soon be an essential line item in most companies’ budgets. MLaaS these days provides full automation of essential, yet time-consuming activities in predictive model construction, such as fast variable selection, variable interaction modeling, and variable transformations or best model selection.
Conclusion – At end and at heart we all now the dirty secret no matter how good the algorithm is, no matter how good I as data scientist, no model can perform magic if direction, intension, time and goal is not set. Combining machine learning with other IT services which can take feed directly and can act on the torrent like softwares for incoming data might end up with the best long-term gains, returns and customer base. Whoever has the best sense for choosing, organizing, uniqueness to combine machine and human skills outlook from the services to collect, clean and label data sets its a market for them that’s just getting started and millions yes billions of dollars are waiting for them. MLaaS is profiled as a collaborative data science platforms that comes with all the tools most data scientists would need, along with a visual interface.
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