Model Deployment
Deploying a model to production is not so different from deploying other software components. It is crucial to automate...
MLOps and DataOps Automation
KFServing
KFServing is an open-source project that provides a Kubernetes-based framework for serving machine learning models. It offers an easy-to-use, standardized solution for deploying, scaling, and managing ML models in production environments.
Features
- Model Serving: Deploy and manage ML models.
- Auto-Scaling: Automatically scales models based on traffic.
- Multi-Framework Support: Supports TensorFlow, PyTorch, XGBoost, and more.
- Canary Rollouts: Allows for gradual model updates.
Benefits
- Efficiency: Simplifies the deployment process.
- Scalability: Automatically handles varying workloads.
- Flexibility: Supports multiple ML frameworks.
- Reliability: Ensures smooth rollouts and updates.
Use Cases
- Deploying machine learning models in production.
- Scaling ML models to handle increased traffic.
- Performing A/B testing with canary rollouts.
- Managing multi-framework ML environments.
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