Operations | Monitoring | ITSM | DevOps | Cloud

December 2024

Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain - Part 2

In this blog series, we will explore the importance of merging DevOps best practices with MLOps to bridge this gap, enhance an enterprise’s competitive edge, and improve decision-making through data-driven insights. Part one discussed the challenges of separate DevOps and MLOps pipelines and outlined a case for integration.

From Challenges to Strategy: Preparing for AI Success in 2025

This webinar features exclusive findings from the 2024 State of AI & LLMs Report and is designed to help you navigate today’s AI/ML challenges to effectively plan for 2025. Guy Levi (VP, Architects Lead) and Guy Eshet (Senior Product Manager, JFrog ML) will explore key trends and discuss how a unified platform that integrates between MLOps and DevOps can make a real difference for your organization when it comes down to security, efficiency and more.

Defending the Holidays: Avoiding the On-Call Pager this December

Three years after Log4Shell made headlines, its impact is still a wake-up call for developers and organizations worldwide. Are your systems secure? In this video, we break down: Stay ahead of vulnerabilities and protect your holidays with actionable insights and a quick demo of the JFrog Platform. Watch now to defend your systems and avoid those on-call holiday headaches!

JFrog Cloud: Architected for Performance at Scale

Petabytes of monthly data transfer. Thousands of concurrent requests per customer. Hundreds of thousands of requests per minute per customer. The JFrog Platform is a mission critical piece of software development and delivery infrastructure for companies that require performance at scale. When you’re supporting thousands of developers, even a minute of downtime or delay can mean millions of dollars lost productivity.

Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain - Part 1

As businesses realized the potential of artificial intelligence (AI), the race began to incorporate machine learning operations (MLOps) into their commercial strategies. But the integration of machine learning (ML) into the real world proved challenging, and the vast gap between development and deployment was made clear. In fact, research from Gartner tells us 85% of AI and ML fail to reach production.