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.
Relational databases in the 1980s were typically designed using the Codd-Date rules for data normalization. It was the most efficient way to store data used… Read More »DSC Webinar Series – Death of the Star Schema.mp4
Today’s enterprises struggle to unlock real value out of data that is stuck in legacy relational databases, data warehouses and data lakes. Modernizing disparate data… Read More »DSC Webinar Series: Optimize Your Data Fabric with Hitachi Vantara and MongoDB Atlas
When it comes to data and analytics investments, organizations are looking to achieve better and faster decisions. Data science/IT teams need to effectively collaborate to… Read More »DSC Webinar Series: Developing, Deploying and Managing Models at Scale
Let’s be honest: The way we’ve been managing data for the past 30 years hasn’t fundamentally changed. Yes, the shift to the cloud and the… Read More »The Data Product ABCs – A Framework for Bringing Product Thinking to Data
Introduction We are in a time when information is the core element of business success for companies in almost any industry. As technologies emerge and… Read More »Why You Need an Augmented Data Integration Tool
AI has been making a lot of noise of late, especially in the context of software development. Of course, this topic is quite wide, but… Read More »Artificial Intelligence: Benefits for Automation Testing
Real-world production ML systems consist of two main components: data and code. Data is clearly the leader, and rapidly taking center stage. Data defines the… Read More »Data Discovery for ML Engineers
When such a sophisticated, risky, and complex technology like AI takes our lives by storm, a clearly defined set of rules on its usage is… Read More »What to Do About the New AI Regulation?
In this short blog, we’ll review the process of taking a POC data science pipeline (ML/Deep learning/NLP) that was conducted on Google Colab, and transforming… Read More »Google Colab to a Ploomber pipeline: ML at scale
In this short article, I’ll try to capture the main differences between the MLops tools Ploomber and Kubeflow. We’ll cover some background on what is… Read More »Ploomber vs Kubeflow: Making MLOps Easier
