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Understanding JFrog ML Deployment Capabilities 0 hr 3 min
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Diverse Deployment Options: Real-time, Batch, and Streaming 0 hr 2 min
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Deploying a Real-time ML Model: Configuration and Strategies 0 hr 5 min
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In-Platform Model Testing and API Interaction 0 hr 1 min
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Real-time Observability: Performance Metrics and Latency Analysis 0 hr 4 min
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Drift Detection and Distribution Analysis 0 hr 2 min
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Simplifying MLOps: Seamless Model Lifecycle and Continuous Improvement 0 hr 1 min
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Knowledge Check
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Deploying and Monitoring ML Models with JFrog ML
This course teaches you how to seamlessly deploy, monitor, and manage machine learning models using JFrog ML. Learn how to take trained models to production with real-time, batch, or streaming deployment options - all within a unified platform that simplifies ML workflows.
Welcome to Deploying and Monitoring ML Models with JFrog ML !
In this course, you’ll explore hands-on deployment strategies like blue-green and shadow deployments, configure auto-scaling resources, and interact with deployed models via API.
The course also covers in-depth observability, including performance metrics, latency analysis, and system utilization.
Finally, you’ll set up drift detection and distribution monitoring to ensure your models stay accurate and reliable over time.
Whether you're a data scientist or MLOps engineer, this course gives you the tools to run production-grade ML at scale, without the infrastructure headaches.
What You’ll Learn:
- Unified deployment workflows with JFrog ML
- Real-time, batch, and streaming deployment strategies
- Advanced rollout techniques (shadow/blue-green deployments)
- Model testing and API interaction
- Real-time observability and performance monitoring
- Data drift detection and feature distribution analysis
- Best practices for scalable, production-ready ML services