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.
For our 4th episode of the AI Think Tank Podcast, we explored cybersecurity and artificial intelligence with the insights of Tim Rohrbaugh, an expert whose career has traversed the Navy to the forefront of commercial cybersecurity. The discussion focused on the strategic deployment of AI in cybersecurity, highlighting the use of open-source models and the benefits of local deployment to secure data effectively.
Presentation and discussion with Suman Aluru and Caleb Stevens In the latest episode of the “AI Think Tank Podcast,” I had the pleasure of hosting… Read More »Retrieval augmented fine-tuning and data integrations
After playing with GPT for some time, testing GenAI vendor solutions, designing my own, and reading feedback from other users, I uncovered a number of… Read More »Towards Better GenAI: 5 Major Issues, and How to Fix Them
As businesses struggle with more data sources and destinations than ever, they strive to bring governance, security, and efficiency to their data ops. To address… Read More »DSC Webinar Series: How to Scale NiFi Deployments to Enable Universal Data Distribution
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In the first part here, I discussed missing, outdated and unobserved data, data that is costly to produce, as well as dirty, unbalanced and unstructured… Read More »15 Data Issues and How to Fix Them – Second Part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
