Machine learning operations (MLOps) has become the backbone of efficient artificial intelligence (AI) development. Blending ML with development and operations best practices, MLOps streamlines deploying ML models via continuous testing, updating, and monitoring. But as ML and AI use cases continue to expand, a need arises for specialized tools and best practices to handle the particular conditions of complex AI apps — like those using large language models (LLMs).

In the rapidly changing world of technology, effective monitoring is critical for maintaining your infrastructure and ensuring it performs effectively. While traditional monitoring methods are effective, they can fall short as systems scale and become more dynamic and complex. This article aims to bridge the gap by introducing software engineers to the power of machine learning (ML) in infrastructure monitoring, outlining not just the ‘how’ but the ‘why’ of its application.

We’ve been exploring new approaches to make Sentry issues more actionable. Read on to learn how we’re using machine learning to improve issue creation (i.e. grouping) and alerts, with as little configuration as possible, so you can focus on fixing the most important issues.

Machine learning is a powerful tool that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Two fundamental approaches to machine learning are supervised and unsupervised learning. In this blog post, we'll explore the key differences between these two approaches, along with examples of their applications.

For a long time, AI was almost exclusively the plaything of science fiction writers, where humans push technology too far, to the point it comes alive and — as Hollywood would have us believe — starts to wreak havoc. Cheery stuff! However, in recent years, we’ve seen an explosion of AI and machine learning technology that, so far, has shown us a fun side with people using AI for creating, planning, and ideating in a big way.