We’re excited to reveal our latest effort towards simplifying and accelerating the test data management process: AI Synthetic Data Generation, part of Redgate Test Data Manager. Officially introduced in a session at the recent PASS Data Community Summit, the capability uses machine learning to rapidly generate realistic yet entirely synthetic data – all while maintaining data integrity and with data privacy built-in as priority.

Building an AI proxy for our AI features was one of the best decisions we made a year ago. In this article, we will share why and what challenges we faced.

It’s hard not to speak in cliches when we talk about artificial intelligence (AI). Today, AI seems to be all around us. And whatever its cultural impact, its rapid evolution is leading to widespread adoption across industries. Much of the discourse focuses on what machine intelligence can do to enrich our lives and businesses. But less has been said about data, and how every AI system relies on it to operate.

For the past two years, AI enthusiasts have been treated to a nonstop procession of lightspeed innovations. ChatGPT rang the era in with a bang; millions of blogs, emails, trivia questions, and sonnets later, Microsoft etched the writing on the wall with its $10 billion investment in OpenAI, ChatGPT’s progenitor. Since then, companies from Alphabet to Zoom have scrambled to somehow, some way, integrate AI into their core offerings.

The demand for LLM is rapidly increasing—it’s estimated that there will be 750 million apps using LLMs by 2025. As a result, the need for LLM observability and monitoring tools is also rising. In this blog, we’ll dive into what LLM monitoring and observability are, why they’re both crucial and how we can track various metrics to ensure our model isn’t just working but thriving.

Cribl employees are renowned for their insatiable curiosity, especially when it comes to their passions. Having been a technical writer for most of my adult life, this goat is deeply passionate about two things: writing engaging content and understanding the mindset of our users. As one of our founders always says, “Software is a people business.” To make my users successful, I need to know how they think. But what if the “user” is a machine? This goat is intrigued.

GenAI apps, also known as Intelligent Apps, are the latest formulation of AI-capable applications. Unlike “Traditional” AI apps that utilize pre-defined processes in a chain to deliver predictive answers, GenAI apps create new and unique interactions with end users. Due to their creative and autonomous nature, integrating generative AI models into apps is like embedding a living brain into a piece of software.

From cloud to edge, hardware-agnostic AI/ML In January Canonical will reconfirm its presence at CES 2025; here we will be showing a cutting-edge AI/ML demo that showcases how AI models can be trained, deployed, and updated seamlessly across various hardware platforms.

Sumo Logic Mo Copilot is a natural language assistant that helps first responders derive insights from logs and resolve issues faster using contextual suggestions and plain English queries. It has been in preview since May 2024 with dozens of customers. Choosing a foundation model was a critical step in its development. Let’s explore our high-level requirements for Copilot, the role of foundation models and the rationale for standardizing on Amazon Bedrock.