JFrog Live Hands-On
MLOps Masterclass

Securing AI/ML Development in the Age of DeepSeek

Foundation models have drastically changed the way data scientists and AI developers approach machine learning with new foundation models being released with increasing regularity. But how do you know if that open source model you’re building your new AI service on top of is secure and trusted? 

In this webinar we explore how to safely use open source and foundation models by leveraging DevSecOps best practices in AI/ML development. We’ll get hands on with examples and best practices that will be publicly available for further testing on your own. 

Doing Experiment Tracking Right

Running experiments is a core activity of Data Scientists to create and improve models. Tracking how those experiments are run is essential for easy comparison, analysis and reproducibility of experiments. However, as important as experiment tracking is, many data science, ML engineers and AI developers struggle to do so in an effective and efficient way. 

In this session we will cover new and enhanced experiment tracking features of JFrog ML, showing how to use them and the benefits they offer. We’ll also touch on any new and upcoming features.

Your Entire AI/ML Lifecycle in a Single Unified Solution

Dive into the world of deploying AI/ML applications to production with JFrog ML. Join our live demo to see how JFrog ML streamlines your AI/ML operations—from GenAI and LLMs to classic ML models, handling your entire AI/ML lifecycle.

Discover how MLOps, LLMOps, and DataOps come together in a unified solution to simplify model development, versioning, and deployment. See firsthand how to manage complex AI/ML projects efficiently and bring innovations to market faster.

Uniting AI/ML and Traditional Software Supply Chains

Uniting the AI/ML and traditional software supply chain starts by operating from a single source of truth. Your AI/ML artifacts should live alongside your traditional software artifacts. The standard practices your DevOps team have implemented for building and deploying traditional software applications should be applied to AI/ML development and deployment. 

With JFrog’s new Machine Learning Repository and FrogML SDK you can easily bring DevOps practices into your AI/ML workflows with zero disruption for Data Science and AI developers. This hands-on webinar will show you how to build and store your models with JFrog as your advanced model registry. See how JFrog makes it easy to version, promote, and distribute models.