DataOps is a philosophy whereby the various stages of development, from analysis to design to development to testing and maintenance, are increasingly integrated through software or machine-learning based systems. Articles in this section focus on both DataOps as a philosophy and business practice, as well as explore deeper technical aspects that make DataOps feasible.
We must have read tons of literature on developing analytics models and finding information/insights out of data blob. Majorly this activity can run stand alone… Read More »Data scientists, you can’t not-know APIs & Dockers
In this post, we explore two terms which are becoming relatively common in professional machine learning applications – MLOps and DevOps The term MLOps refers… Read More »MLOps vs. DevOps: the similarities and differences
Via Nitish Tiwari Kubernetes has fundamentally altered the traditional application development and deployment patterns. Application development teams can now develop, test and deploy their apps… Read More »Kubernetes Storage Patterns
Software is always not only a set of instructions but also a context that manages, interacts and executes these instructions. At the start of developing,… Read More »Docker in 10 minutes
By Pranay Agarwal, VP, Sales and Customer Success at Analytics2go The theme of artificial intelligence adoption is becoming the competitive edge for many companies in… Read More »Avoid Common Pitfalls in Launching AI Projects
As anyone involved in the developing world would tell you, the app’s UI is one of its most critical elements, if not the most important.… Read More »Web UI Development: Why Vue.js is the Idea Choice
