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Machine Learning

sumologic

Crossing the machine learning pilot to product chasm through MLOps

Apr 9, 2024 By Bashyam Anant, David Andrzejewski and Aaishwarya Bansal In Sumo Logic
Numerous companies keep launching AI/ML features, specifically “ChatGPT for XYZ” type productization. Given the buzz around Large Language Models (LLMs), consumers and executives alike are growing to assume that building AI/ML-based products and features is easy. LLMs can appear to be magical as users experiment with them.
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opsmatters

How AI and Machine Learning are Revolutionizing the Research Process

Apr 3, 2024 By OpsMatters In OpsMatters
The advent of Artificial Intelligence (AI) and Machine Learning (ML) has been nothing short of transformative. Scholars across disciplines are leveraging these technologies to analyze data quickly and accurately, opening new frontiers in knowledge and understanding. The traditional research process, often laborious and time-consuming, is evolving into a more efficient and dynamic practice thanks to the computational power of AI and ML. For example, students who turn to specialized services with requests such as "Write my paper for me" can receive academic papers much faster, thanks to AI.
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civo

How to overcome common challenges in machine learning deployments

Apr 2, 2024 By Josh Mesout In Civo
🚨 To read the full findings from this research, visit The Machine Learning State of Play 2024 white paper. Are the challenges of deploying machine learning (ML) overshadowing its true potential in the modern workplace? Through our recent white paper , we spoke to 500+ developers who have experience working with ML systems to gain an understanding of the pain points faced by developers when using ML solutions.
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circleci

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

Mar 25, 2024 By Jacob Schmitt In CircleCI
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).
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influxdata

Machine Learning and Infrastructure Monitoring: Tools and Justification

Mar 20, 2024 By Charles Mahler / In InfluxData
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.
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zenoss

AI Explainer: Supervised vs. Unsupervised Machine Learning

Mar 15, 2024 By Trent Fitz In Zenoss
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.
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elastic

Machine learning vs. AI: Understanding the differences

Mar 7, 2024 By Aditya Tripathi, In Elastic
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.
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jfrog

Advancing MLOps with JFrog and Qwak

Feb 28, 2024 By Yonatan Arbel In JFrog
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.
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jfrog

Four Key Lessons for ML Model Security & Management

Feb 21, 2024 By Zohar Sacks In JFrog
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.
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