Get yourself trained on Microservices introduction for with this Online Training Microservices introduction for Data Science.
Online Training Microservices introduction for Data Science
The objective of this course of Introduction to Microservices for Data Science is to introduce you to the new DevOps tools for deploying Machine Learning models in a Big Data productive environment. In this course you will learn both the theory and the basic practice that will allow you to understand and apply these new methodologies. We will work with Python, Scikit-Learn, Flask, Docker, Kubernetes and Jenkins, but no previous knowledge is necessary to enjoy this course.This course is for anyone who wants to increase their knowledge in the deployment of Machine Learning models in production, including Data Scientists, Data Architects, developers and product managers.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.