ML industry experts have identified the process and requirements for developing production-ready machine learning models. You'll find in this eBook that a standard approach helps to get more models in production.
AI is encountering another hurdle to delivering value, in the form of friction among and between teams. A survey of 508 machine learning practitioners that included data scientists and engineers found challenges related to people, process, and tools. This friction can cause delays in ML development that delay or halt model deployment to production.
In this report, we perform exploratory data analysis on the HackerNews dataset. Our profiling script builds visualizations, extracts summary profiles, and logs samples of the data for reference. We investigate the relationship between our initial set of features and our target.