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.
There are thousands of articles explaining the differences between data scientist and machine learning engineer. Data science gets broken down even further, with data analysts… Read More »The Rise of the Dual Data Scientist / Machine Learning Engineer
With the release of SAS Container Runtime (SCR), organizations can execute models and decisions outside of SAS using standard technologies. Containerized deployments are lightweight to… Read More »DSC Webinar Series – Best Practices for Adopting Containers within your MLOps Process.mp4
Data scientists spend 80% of their time on data cleaning and exploratory analysis. What if you could automate most of this? What if data scientists… Read More »How to Automate Data Cleaning, in a Nutshell
In the first part here, I discussed missing, outdated and unobserved data, data that is costly to produce, as well as dirty, unbalanced and unstructured… Read More »15 Data Issues and How to Fix Them – Second Part
The amount of customer data available to brands has absolutely exploded in the past decade, giving brands an opportunity to create personalized experiences. But with… Read More »DSC Webinar Series – Its Time to Turn Data Chaos into Order.mp4
Data informs everything in a digital-first world. It is the fuel for enterprise intelligence that helps organizations create new business opportunities and deal with competitive… Read More »DSC Webinar Series: Building Trust in Your Data to Fuel Breakthrough Innovation
How to fix various data issues in a few simple steps? In this first part, I discuss missing, outdated and unobserved data, data that is… Read More »15 Data Issues and How to Fix Them
We live in a data-rich world. Very data rich. Indeed, it’s estimated that roughly 2.5 quintillion bytes of data are created every day. Perhaps because… Read More »Why We Need to Move From Data-First to a Knowledge-First World
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
