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.