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Machine Learning

From MLOps to LLMOps: The evolution of automation for AI-powered applications

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).

Machine Learning and Infrastructure Monitoring: Tools and Justification

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.

AI Explainer: Supervised vs. Unsupervised Machine Learning

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.

Machine learning vs. AI: Understanding the differences

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.

Advancing MLOps with JFrog and Qwak

Modern AI applications are having a dramatic impact on our industry, but there are still certain hurdles when it comes to bringing ML models to production. The process of building ML models is so complex and time-intensive that many data scientists still struggle to turn concepts into production-ready models. Bridging the gap between MLOps and DevSecOps workflows is key to streamlining this process.

Four Key Lessons for ML Model Security & Management

With Gartner estimating that over 90% of newly created business software applications will contain ML models or services by 2027, it is evident that the open source ML revolution is well underway. By adopting the right MLOps processes and leveraging the lessons learned from the DevOps revolution, organizations can navigate the open source and proprietary ML landscape with confidence.

How Machine Learning is Reshaping Your Business

Machine learning, a subset of artificial intelligence (AI), has emerged as a powerful tool for reshaping the landscape of business operations. By leveraging algorithms and statistical models, machine learning enables computers to learn from data and make predictions or decisions without explicit programming. In this blog, we'll delve into the transformative impact of machine learning on various aspects of business, from enhancing customer experience to driving innovation and ensuring data security.

AI vs. ML: What's the Difference? + What is #aiops in 60 Seconds | #backtobasics | LogicMonitor

Ever wonder what #machinelearning (#ml) really means? Or how it's different from #ai? What even is #aiops? This #BackToBasics short explains it ALL in plain English! #shorts Follow us...