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
Testimonials (3)
Craig was extremely involved in the training, always making sure we are paying attention, adapted the examples to our day-to-day activities and always provided an answer when asked, even if the information was not added in the presentation.
Ecaterina Ioana Nicoale - BOOKING HOLDINGS ROMANIA SRL
Course - DevOps Foundation®
High level of commitment and knowledge of the trainer
Jacek - Softsystem
Course - DevOps Engineering Foundation (DOEF)®
real life examples
