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Slow Build Pipeline? Build Faster by Building Only What You Need

In today’s fast-paced software world, organizations need to deliver fast, as fast as multiple times per day. Agile methodologies and DevOps culture have contributed to enabling this constant delivery. Continuous integration and continuous delivery (CI/CD) is an almost basic requirement for any company that wants to remain competitive in the market.

CI/CD allows the delivery of code changes more frequently and reliably by automating the required steps to take a working piece of software to a production environment. This automation comprises a set of steps to compile, build, and deploy code is called a “building pipeline.” 

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The 10 Commandments for Designing a Data Science Project

Introduction

As businesses across industries seek to improve workflows and the delivery of products and services through increased automation, there is an ever-growing demand for the adoption of more advanced data science capabilities and projects. 

Artificial intelligence and machine learning can, of course, deliver great ROI — but only under the right conditions. In every instance, a data science project must be framed in the right way, both from a business and a technical point of view. To help provide this framework, I have devised the following “10 commandments” for designing a data science project.