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Course Outline
Foundations of Production Deployment
- Primary obstacles in deploying fine-tuned models
- Contrasting development versus production environments
- Overview of deployment tools and platforms
Model Preparation for Release
- Exporting models using standard formats (such as ONNX, TensorFlow SavedModel, etc.)
- Tuning models to maximize latency and throughput
- Validating models against edge cases and real-world datasets
Containerization Strategies
- Overview of Docker
- Building Docker images specifically for ML models
- Best practices for maintaining container security and efficiency
Scaling with Kubernetes
- Applying Kubernetes to AI workloads
- Configuring Kubernetes clusters for model hosting
- Managing load balancing and horizontal scaling
Ongoing Monitoring and Care
- Setting up monitoring using Prometheus and Grafana
- Implementing automated logging for error detection and performance tracking
- Establishing retraining pipelines to handle model drift and updates
Security in Production
- Protecting APIs used for model inference
- Implementing robust authentication and authorization mechanisms
- Mitigating data privacy risks
Practical Applications and Labs
- Deploying a sentiment analysis model
- Scaling a machine translation service
- Setting up monitoring for image classification models
Recap and Future Directions
Requirements
- Solid grasp of machine learning workflows
- Proven experience in fine-tuning ML models
- Working knowledge of DevOps or MLOps principles
Intended Audience
- DevOps Engineers
- MLOps Practitioners
- AI Deployment Specialists
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
