Part 1: Engaging ML Tracking and Visualization : Sit Back and Relax

Learn Something New: The Golden Pheasant are popularly known as the ‘Chinese Pheasant’ as they are native to Western and Central China, living primarily in the mountainous forests. From The New York Public Library — https://on.nypl.org/3zwIhFJ

(This is part of a series. Part 2 is published now.)

The world of Machine and Deep Learning (ML & DL) is growing at a fantastic pace. As presented in the “The AI Index 2021 Annual Report” and shown in the following graph, in the year 2020, US researchers alone submitted more than 11k AI-Related publications on arXiv. Combining the numbers from other places like the EU and China, AI and its subcomponents ML & DL are among the most promising and emerging fields in computer science, with wide-ranging applications in other areas like Biology, Physics, and Chemistry.

Geographic Distribution of AI-Related publications on arXiv from 2015–2020 (Data Source: arXiv, 2020; Citation: Daniel Zhang, Saurabh Mishra, Erik Brynjolfsson, et. al. , “The AI Index 2021 Annual Report,” AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, March 2021. https://stanford.io/3i0PEz4 )

Hidden behind all this promise lies the hard work of thousands of AI practitioners. Putting an AI model into production is preceded by a lot of time-intensive and computation-intensive steps, like data collection, data preprocessing, train multiple iterations of the model, optimizing the model, and analyzing the model on untrained data. Successful training of a big model, with millions of trainable and tunable parameters, might take hundreds (sometimes even thousands or more) of iterations. As models get bigger ; while ML/DL computations get faster and we train even more models…