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Duration 14 hours
Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3
- Balancing performance against cost
- Considerations for enterprise-scale operations
LLM Deployment Patterns
- Serving topologies and architectural decisions
- On-premises versus cloud-based deployment
- Hybrid and multi-cloud approaches
Optimising Inference
- Batching techniques for high throughput
- Quantisation methods for cost efficiency
- Accelerator and GPU utilisation
Scalability and Reliability
- Scaling Kubernetes clusters for inference workloads
- Load balancing and traffic management
- Fault tolerance and redundancy planning
Cost Engineering Frameworks
- Evaluating inference cost efficiency
- Right-sizing compute and memory resources
- Monitoring and alerting for continuous optimisation
Security and Compliance in Production
- Securing deployments and APIs
- Data governance implications
- Regulatory compliance within cost engineering
Case Studies and Best Practices
- Reference architectures for large-scale Mistral deployments
- Insights from enterprise implementations
- Emerging trends in efficient LLM inference
Summary and Next Steps
Requirements
- A solid grasp of machine learning model deployment
- Practical experience with cloud infrastructure and distributed systems
- Knowledge of performance tuning and cost optimisation techniques
Target Audience
- Infrastructure engineers
- Cloud architects
- MLOps leads
