Machine Learning Automation - End to End Right from Building Machine Learning Model to App, without or minimal knowledge requirement either in Python or Machine Learning. This course covers Regression, Binary and Multi-Class Classification Problems. No prerequisites required for this course. This course covers Exploratory Data Analysis, Data Cleaning, Model Pipeline, Metrics and Saving Model and thereafter Building App.

This article explains how Machine Learning Operations came to be a discipline inside many companies and things to consider when deciding if your organization is ready to form an MLOps team. Machine learning (ML) is a subset of artificial intelligence in which computer systems autonomously learn a task over time. Based on pattern analyses and inference models, ML algorithms allow a computer system to adapt in real time as it is exposed to data and real-world interactions. For many people, ML was, until recently, considered science fiction. But advances in computational power, frictionless access to scalable cloud resources, and the exponential growth of data have fueled an increase in ML-based applications.

For many companies, today, the demand for data and analytics is now everywhere in the enterprise. Projects are underway to improve customer engagement, reduce risk and optimize business operations. Data sources are also growing rapidly with new data coming from both inside and outside the enterprise in many different varieties. Also although analytics are needed almost everywhere, the current approaches to developing them are slow and expensive. To support this demand, the modern analytical environment has also expanded going way beyond the traditional data warehouse to become an analytical ecosystem comprising multiple data stores and platforms optimised for different kinds of analytic workloads.

Known for delivering out-of-the-box, performance based strategies in the marketing and technology domain, Envigo has footprints in India and UK. Led by Saurabh Kumar, an alumni of National Institute of Technology, Karnataka and IIM, Ahmedabad, Envigo partners with over 90 clients world-wide, worked with more than 300 brands so far, across 24 countries and 4 continents, adding 10 to 15 more clients in the hospitality sector by the end of 2017. Talking about the use of Artificial Intelligence (AI), Big Data & Automation, Kumar feels that the use of AI in predictive and backward-facing analysis presents big opportunities for digital agencies. "ROI focussed agencies are looking to implement AI in programatic Ads and personalisation for customers. So, we understand that all the visitors are not in the same phase of buying cycle. Be it web, mobile or any other channel, there is a need to understand what the customer requires. Hence, capturing customer actions, actionable insights and creating cross platform user experience becomes prime focus. Once we gather enough information, we can target customers and capitalise by capturing micro moments. With use of advanced analytics, marketing automation, custom web and mobile solutions, we have been able to shift from Communication to Engagement Strategies which can be implemented on all connected devices."

The general idea here is that the work done by a Machine Learning engineer can be automated, thus freeing potential users from the tyranny of needing to have specific expertise. Presumably, the ultimate goal of such automations is to make Machine Learning accessible to more people. After all, if a thing can be done automatically, that means anyone who can press a button can do it, right? I'm going to make a three-part argument here that "Machine Learning Automation" is really just a poor proxy for the true goal of making Machine Learning useable by anyone with data. Furthermore, I think the more direct path to that goal is via the combination of automation and interactivity that we often refer to in the software world as "abstraction". By understanding what constitutes a powerful Machine Learning abstraction, we'll be in a better position to think about the innovations that will really make Machine Learning more accessible.

The general idea here is that the work done by a Machine Learning engineer can be automated, thus freeing potential users from the tyranny of needing to have specific expertise. Presumably, the ultimate goal of such automations is to make Machine Learning accessible to more people. After all, if a thing can be done automatically, that means anyone who can press a button can do it, right? I'm going to make a three-part argument here that "Machine Learning Automation" is really just a poor proxy for the true goal of making Machine Learning useable by anyone with data. Furthermore, I think the more direct path to that goal is via the combination of automation and interactivity that we often refer to in the software world as "abstraction". By understanding what constitutes a powerful Machine Learning abstraction, we'll be in a better position to think about the innovations that will really make Machine Learning more accessible.