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Machine Learning

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...

Unlock the Secrets of Machine Learning: A Beginner's Guide with Josh Mesout - Navigate Europe 23

Dive into the world of machine learning with Josh Mesout. This video is a great starting point for beginners, offering a practical approach to understanding and applying machine learning concepts. Follow along as Josh demonstrates setting up a machine learning environment on Civo and explores a PyTorch notebook for handwriting recognition. Whether you're coding along or just watching, this session is packed with useful tips and resources for your machine learning journey. Don't forget to check out our GitHub repository for additional materials and join the conversation in the comments!

AI, Privacy and Terms of Service Updates

Like everyone else in the world, we are thinking hard about how we can harness the power of AI and machine learning while also staying true to our core values around respecting the security and privacy of our users’ data. If you use Sentry, you might have seen our “Suggested Fix” button which uses GPT-3.5 to try to explain and resolve a problem. We have additional ideas being developed as well that we’re excited to preview.

Leveraging Argo Workflows for MLOps

As the demand for AI-based solutions continues to rise, there’s a growing need to build machine learning pipelines quickly without sacrificing quality or reliability. However, since data scientists, software engineers, and operations engineers use specialized tools specific to their fields, synchronizing their workflows to create optimized ML pipelines is challenging.

Unlock the power of network forecasting with machine learning

In the dynamic world of IT, traditional network monitoring approaches are no longer sufficient to manage the complexities of today’s networks—be they wired or wireless. To stay ahead of network events, IT administrators must shift from being reactive to adopting a proactive stance. This transition involves a comprehensive approach to network monitoring that includes forecasting future network requirements with the help of machine learning (ML) technology.

ML and APM: The Role of Machine Learning in Full Lifecycle Application Performance Monitoring

The advent of Machine Learning (ML) has unlocked new possibilities in various domains, including full lifecycle Application Performance Monitoring (APM). Maintaining peak performance and seamless user experiences poses significant challenges with the diversity of modern applications. So where and how does ML and APM fit together? Traditional monitoring methods are often reactive, resolving concerns after the process already affected the application’s performance.