With availability of massive data and computation, Machine Learning (ML) and other spheres of artificial intelligence are growing at rapid rate. AI has become the demand of time and the need of the hour. To keep up, almost every company is either starting a new Data Science/Machine Learning department or expanding rapidly with multiple projects in pipeline. Now, we have more ML competitions and hackathons than ever recorded in the history.Everyday there are new courses focusing entirely on Python libraries and Machine Learning APIs. People are sharing latest machine learning (ML) algorithms, computations, graphs, charts and code snippets on a daily basis focusing technical aspects and implementations.

Given the overload of information towards technical aspects , less focus is on Machine Learning project discovery session or requirement gathering session which focuses on business aspects of the problem. Being in this field for past few years, I have seen many successes and failures of ML projects. I strongly believe, the project requirement or discovery session is one of the prime deciders between success and failure of any ML project like any other project.

So, let’s start out journey towards making the complex simple and enhance ML productivity of your team by 10x.

What is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) that involves finding patterns and relationships between input and output data attributes using historical data and produces an optimized mathematical function (also called model) holding the relationship. The model is then used for predicting the output on new data.

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