JFrog Live Hands-On
MLOps Masterclass
Stay on top of emerging MLOps trends, model security, and AI/ML development practices with this educational live learning series.
Starting a model from scratch with a secure foundation
When creating a new model it’s crucial that it is not only accurate and efficient, but also secure and easily manageable for deployment and inference testing. Join us for an in-depth webinar where you will learn how to build a robust ML model from scratch with a strong emphasis on security and scalability.
Whether you’re just starting your journey as a Data Scientist or ML Engineer, or you’re looking to refine your skills in secure model development and deployment, this webinar will equip you with the knowledge and practical skills to succeed. This session will help ensure your models are built on a secure foundation from the ground up.
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.
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.
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.
