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Course Outline
Introduction to Production Deployment
- Key challenges in deploying fine-tuned models.
- Differences between development and production environments.
- Tools and platforms for model deployment.
Preparing Models for Deployment
- Exporting models in standard formats (ONNX, TensorFlow SavedModel, etc.).
- Optimizing models for latency and throughput.
- Testing models on edge cases and real-world data.
Containerization for Model Deployment
- Introduction to Docker.
- Creating Docker images for ML models.
- Best practices for container security and efficiency.
Scaling Deployments with Kubernetes
- Introduction to Kubernetes for AI workloads.
- Setting up Kubernetes clusters for model hosting.
- Load balancing and horizontal scaling.
Model Monitoring and Maintenance
- Implementing monitoring with Prometheus and Grafana.
- Automated logging for error tracking and performance.
- Retraining pipelines for model drift and updates.
Ensuring Security in Production
- Securing APIs for model inference.
- Authentication and authorization mechanisms.
- Addressing data privacy concerns.
Case Studies and Hands-On Labs
- Deploying a sentiment analysis model.
- Scaling a machine translation service.
- Implementing monitoring for image classification models.
Summary and Next Steps
Requirements
- A strong understanding of machine learning workflows.
- Experience in fine-tuning ML models.
- Familiarity with DevOps or MLOps principles.
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
