The Definitive Guide to Delivering Value from Data using DataOps
This is an opinionated guide on how to use DataOps to deliver business value from data. It is a prescriptive fix for organizations struggling to…
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5 Lessons Data Scientists Need to Know About Moving Models to Production
This article shares the common issues that data teams face when moving their models from development to production systems and how these issues could be…
A Checklist to Design Your Data Platform
Have you done a “digital transformation” but find your business fails to get real value from your data? Are you finding new data-related pain points,…
Using R Plumber and Slack to Run Data Science Pipelines
Using R Plumber and Slack to Run Data Science Pipelines Last year I published a starter guide that explains how we used the R…
Tech Sprints Speed Runs: How to Validate your Ideas as Fast as Humanly Possible
Are you building products without knowing if anyone will use them? Does market validation seem to take forever? If so, then this post about how…
Serverless Principles by Example
Do you have server infrastructure pain? Are your engineers spending more time managing servers than they are enabling more business value? Are you paying for…
An Inside Look at DataOps at Retina
How can DataOps help improve efficiency at an AI or machine learning company? At Retina, we follow several DataOps principles in order to empower our…
DataOps Principles: How Startups Do Data The Right Way
If you have been trying to harness the power of data science and machine learning —but, like many teams, struggling to produce results— there’s a…