Operations | Monitoring | ITSM | DevOps | Cloud

The speed of today’s software development lifecycle is only getting faster. However, the complexity of today’s pipelines make it hard to track and manage the processes software releases must go through. With increasing regulatory pressures, ensuring and proving your software has gone through the necessary quality controls is no longer nice to have – it has become a necessity.

Just like in traditional software development, creating AI applications isn’t a one size fits all approach. However, many of the challenges and concerns facing AI/ML development teams share common threads – difficulties getting models to production, tangled infrastructure, data quality, security issues, and so on.

The synergy between DevOps and MLOps is more crucial now than ever. However, merging these two paradigms into a coherent software supply chain poses a unique set of challenges that can leave teams feeling overwhelmed. From the complexities of managing model dependencies to adapting conventional CI/CD tools for advanced machine learning (ML) workflows, the path to integration isn’t without its twists and turns.

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.

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.

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.

We can all agree that visibility into resource usage is crucial for optimizing performance and managing costs to drive your business — especially in today’s cloud-driven world. MyJFrog is a comprehensive management portal for overseeing JFrog cloud platform instances and subscriptions. It provides a centralized control tower to manage and monitor subscriptions, resources, and usage.

I think we can all agree that, in general, different users have different needs. For instance, we’ve found that developers generally use Artifactory to find, select, and then install packages into their development environment, while administrators tend to use Artifactory for troubleshooting, confirming package operations, and other related analyses.

swampUP 2024, the annual JFrog DevOps Conference, was unique in it’s addressing not only more familiar DevOps and DevSecOps issues, but adding specific operational challenges, stemming from the explosive growth of GenAI and the resulting need for specialized capabilities for handling AI models and datasets, while supporting new personae such as AI/ML engineers, data scientists and MLOps professionals.

Feature stores are rapidly growing in popularity as organizations look to improve their machine learning productivity and operations (MLOps). With the advancements in MLOps, feature stores are becoming an essential component of the machine learning infrastructure, helping organizations to improve the performance and ability to explain their models, and accelerate the integration of new models into the production.