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Duration 21 hours
Course Outline
Foundations of Production Deployment
- Primary obstacles in deploying fine-tuned models
- Distinguishing between development and production settings
- Essential tools and platforms for model release
Preparing Models for Release
- Exporting models to standard formats (ONNX, TensorFlow SavedModel, etc.)
- Refining models for improved latency and throughput
- Validating models against edge cases and actual user data
Containerization Strategies
- Overview of Docker
- Building Docker images for ML models
- Key best practices for container security and resource efficiency
Scaling with Kubernetes
- Kubernetes fundamentals for AI workloads
- Configuring Kubernetes clusters for model hosting
- Managing load balancing and horizontal scaling
Model Monitoring and Upkeep
- Setting up monitoring using Prometheus and Grafana
- Automating logs for error detection and performance analysis
- Establishing retraining pipelines to handle model drift and updates
Production Security Protocols
- Protecting APIs for model inference
- Implementing authentication and authorization controls
- Mitigating data privacy risks
Practical Case Studies and Labs
- Deploying a sentiment analysis model
- Scaling a machine translation service
- Configuring monitoring for image classification models
Recap and Future Directions
Requirements
- Solid grasp of machine learning processes
- Practical experience in fine-tuning ML models
- Knowledge of DevOps or MLOps methodologies
Target Audience
- DevOps engineers
- MLOps practitioners
- AI deployment specialists
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
