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Course Outline
Introduction to Production Deployment
- Key challenges in deploying fine-tuned models
- Differences between development and production environments
- Tools and platforms for model deployment
Preparing Models for Deployment
- Exporting models in standard formats (ONNX, TensorFlow SavedModel, etc.)
- Optimizing models for latency and throughput
- Testing models on edge cases and real-world data
Containerization for Model Deployment
- Introduction to Docker
- Creating Docker images for ML models
- Best practices for container security and efficiency
Scaling Deployments with Kubernetes
- Introduction to Kubernetes for AI workloads
- Setting up Kubernetes clusters for model hosting
- Load balancing and horizontal scaling
Model Monitoring and Maintenance
- Implementing monitoring with Prometheus and Grafana
- Automated logging for error tracking and performance
- Retraining pipelines for model drift and updates
Ensuring Security in Production
- Securing APIs for model inference
- Authentication and authorization mechanisms
- Addressing data privacy concerns
Case Studies and Hands-On Labs
- Deploying a sentiment analysis model
- Scaling a machine translation service
- Implementing monitoring for image classification models
Summary and Next Steps
Requirements
- Strong understanding of machine learning workflows
- Experience with fine-tuning ML models
- Familiarity with DevOps or MLOps principles
Audience
- DevOps engineers
- MLOps practitioners
- AI deployment specialists
21 Hours
Testimonials (1)
There were many practical exercises supervised and assisted by the trainer
