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Course Outline
Foundations of Production Deployment
- Critical obstacles in deploying fine-tuned models
- Distinct differences between development and production settings
- Platforms and tools facilitating model release
Readiness for Model Deployment
- Exporting models via standard formats such as ONNX and TensorFlow SavedModel
- Enhancing models for optimal latency and throughput
- Validating models against edge cases and real-world datasets
Model Deployment via Containerization
- Foundations of Docker
- Generating Docker images for ML workloads
- Optimal practices for container security and resource efficiency
Scaling with Kubernetes
- Applying Kubernetes to AI workloads
- Configuring Kubernetes clusters for model hosting
- Implementing load balancing and horizontal scaling
Ongoing Model Monitoring and Maintenance
- Deploying monitoring solutions with Prometheus and Grafana
- Automated logging for performance tracking and error diagnosis
- Establishing retraining pipelines to handle model drift and updates
Production Security Assurance
- Hardening APIs for model inference
- Configuring authentication and authorization frameworks
- Mitigating data privacy risks
Practical Examples and Lab Work
- Releasing a sentiment analysis model
- Scaling a machine translation service
- Setting up monitoring for image classification models
Recap and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Practical experience in fine-tuning ML models
- Adeptness with DevOps or MLOps methodologies
Target Audience
- DevOps engineers
- MLOps practitioners
- Specialists in AI deployment
21 Hours
Testimonials (2)
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
