JFrog ML is a platform designed to manage the complete AI/ML lifecycle from development to production. The solution combines MLOps, AI Catalog, and Feature Store capabilities to support data scientists, ML engineers, and AI developers in deploying AI and ML services. The platform provides model training, deployment, and monitoring capabilities for various model types including GenAI, LLMs, and traditional ML models. Users can train and fine-tune models, then deploy them at scale through live API endpoints, Kafka streams, or batch inference. The system includes real-time monitoring and alerting for deployed models. The AI Catalog component consolidates AI models in a unified hub with centralized control, security, and regulatory compliance features. The Feature Store manages feature engineering and data pipelines, allowing users to ingest and process data from multiple sources. JFrog ML integrates with existing DevSecOps workflows and supports multi-cloud deployments across AWS, Google Cloud, and Microsoft Azure. Organizations can deploy on JFrog's infrastructure or their own cloud environment. The platform supports A/B testing for model deployments and provides feature lifecycle management capabilities. The solution aims to bring DevOps practices to AI/ML development, enabling collaboration between DevOps, security teams, ML engineers, data scientists, and product managers within a single platform.