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.

JFrog has long supported standards widely used by developers, including OCI container images. We started with our OCI-compliant Docker registry, then followed up with dedicated JFrog Artifactory OCI repositories. In our continued commitment to developer freedom of choice, we’re excited to take another leap forward. JFrog is now fully conformant to OCI v1.1. Source: OCI Conformance Page JFrog is now fully certified to the OCI v1.1 standard.

The AI revolution is transforming enterprises faster than you can say, “sudo apt-get install skynet.” According to McKinsey, 65% of organizations now regularly use generative AI, nearly doubling from last year. However, as developers rush to integrate AI into their products, the shift from AI proof-of-concept to production can feel like trying to assemble flat-box furniture in a hurricane.

In the fast-paced, ever-evolving world of software development, the ability to seamlessly migrate and manage workloads on the cloud is a game changer. At JFrog, we’re committed to empowering organizations to achieve their DevOps, DevSecOps, and MLOps goals with speed, security, and efficiency. Migrating these workloads to the cloud offers numerous advantages, including increased scalability, cost efficiency, and improved agility.

The software development industry reacted with excitement to the news about the partnership between JFrog and GitHub and its potential impact on software development operations as covered in our recent webinar.