How to Scale Today’s Data Science Initiatives
Learn how to define your key business objectives, manage the way you scale your ML initiatives, organize your DS team, and build out your processes and infrastructure
Standardizing the ML Experimentation Process
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.
Building Effective ML Teams
This eBook explains the three critical traits of successful ML teams, and the risks of missing any of these items: visibility, reproducibility, and collaboration.
The Ultimate Guide to MLOps
MLOps provides massive returns when organizations develop a robust and efficient system. Learn how to create a stronger workflow with this ultimate guide.
2021 ML Practitioner Survey
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.
Standardizing the Experiment #3: Understanding the Data
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.
Standardizing the Experiment #2: Baseline Models (Performance Prediction)
As part of the Standardizing the Experiment Series, we present a report on a baseline post Performance Prediction Model for the HackerNews Dataset.