The Evolution of LLMOps: Adapting MLOps for GenAI

In recent years, machine learning operations (MLOps) have become the standard practice for developing, deploying, and managing machine learning models. MLOps standardizes processes and workflows for faster, scalable, and risk-free model deployment, centralizing model management, automating CI/CD for deployment, providing continuous monitoring, and ensuring governance and release best practices.

Developing Agile ETL Flows with Ballerina

Organizations generate vast amounts of data daily during various business operations. For example, whenever a customer checks out out at a retail outlet, data such as the customer identifier, retail outlet identifier, time of check out, list of purchased items, and the total sales value can be captured in the Point of Sales (PoS) system. Similarly, field sales staff may record possible sales opportunities in spreadsheets.

Leveraging Snowflake And AI To Create Personalized Customer Experiences At Scale

In this episode of the "Data Cloud Podcast", Bill Stratton, Global Head of Media, Entertainment & Advertising at Snowflake, sits down with Ravi Kandikonda, Sr. VP of Marketing at Zillow. Ravi shares his experiences and insights on modern software development, talks about how his academic background prepared him for modern marketing, and what Zillow is doing to approach personalization at scale.

Qlik Anonymous Access - SaaS in 60

Qlik Anonymous Access is an exclusive, new capability that enables organizations to share analytics insights with a public audience easily. It leverages Qlik Cloud’s secure and scalable platform, allowing you to embed Qlik Sense apps, dashboards, and visualizations into websites or third-party applications using shareable links or our new Qlik Embed APIs. With Anonymous Access, no login credentials are required, simplifying engagement with embedded analytics.

How Thrivent Uses Real-Time Data for AI-Driven Fraud Detection

In today’s fast-paced financial services landscape, customers have a shorter attention span than ever. To meet clients’ growing demands for real-time access to information and keep innovating in areas like fraud detection and personalized financial advice, Thrivent needed to overhaul its data infrastructure. With data scattered across siloed legacy systems, diverse tech stacks, and multiple cloud environments, the challenge was a bit daunting. But by adopting Confluent Cloud, Thrivent was able to unify its disparate data systems into a single source of truth.