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Course Outline

Foundations of Production Deployment

  • Core challenges in deploying fine-tuned models
  • Distinguishing between development and production environments
  • Overview of tools and platforms for model deployment

Model Preparation for Deployment

  • Exporting models using standard formats (e.g., ONNX, TensorFlow SavedModel)
  • Optimizing models for improved latency and throughput
  • Testing model robustness against edge cases and real-world data

Containerization Strategies for Models

  • Foundational concepts in Docker
  • Building Docker images specifically for ML models
  • Best practices for maintaining container security and efficiency

Scalable Deployments via Kubernetes

  • Introducing Kubernetes for AI workloads
  • Configuring Kubernetes clusters for model hosting
  • Managing load balancing and horizontal scaling

Ongoing Model Monitoring and Maintenance

  • Setting up monitoring using Prometheus and Grafana
  • Automating logging for error detection and performance tracking
  • Establishing retraining pipelines to address model drift and updates

Security Assurance in Production

  • Securing APIs dedicated to model inference
  • Implementing robust authentication and authorization mechanisms
  • Mitigating data privacy risks and concerns

Applied Case Studies and Practical Labs

  • Deploying a sentiment analysis model
  • Scaling a machine translation service
  • Setting up monitoring for image classification models

Course Conclusion and Future Directions

Requirements

  • A solid grasp of machine learning workflows
  • Practical experience in fine-tuning ML models
  • Working knowledge of DevOps or MLOps principles

Intended Audience

  • DevOps Engineers
  • MLOps Specialists
  • AI Deployment Professionals
 21 Hours

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