Data, Infra and Algorithms
Today (tail end of 2019), the rate of adoption of machine learning has crossed the initial curve in most leading technology organizations across the globe. Here is a great article on deployment of ML by…
Read more of Data management Series: What matters most in Data lifecycle for ML applications
In this first blog post (of a series), I will cover key principles involving sourcing of data; primarily for training and validating machine learning models. This article focuses on “what” to do rather than “how”…
Read more of Part 1: Data acquisition principles, cost and more insights
In continuation of part 1 blog post, for most machine learning tasks, a dataset is needed to train and test the output. Arguably, preparing the right datasets is the most critical step to get the…
Read more of Part 2: ML Data acquisition and data sourcing strategies