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Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3
- Performance versus cost trade-offs
- Enterprise-scale considerations
Deployment Patterns for LLMs
- Serving topologies and design choices
- On-premises versus cloud deployments
- Hybrid and multi-cloud strategies
Inference Optimisation Techniques
- Batching strategies for high throughput
- Quantisation methods for cost reduction
- Accelerator and GPU utilisation
Scalability and Reliability
- Scaling Kubernetes clusters for inference
- Load balancing and traffic routing
- Fault tolerance and redundancy
Cost Engineering Frameworks
- Measuring inference cost efficiency
- Right-sizing compute and memory resources
- Monitoring and alerting for optimisation
Security and Compliance in Production
- Securing deployments and APIs
- Data governance considerations
- Regulatory compliance in cost engineering
Case Studies and Best Practices
- Reference architectures for Mistral at scale
- Lessons learned from enterprise deployments
- Future trends in efficient LLM inference
Summary and Next Steps
Requirements
- Strong understanding of machine learning model deployment
- Experience with cloud infrastructure and distributed systems
- Familiarity with performance tuning and cost optimisation strategies
Audience
- Infrastructure engineers
- Cloud architects
- MLOps leads
14 Hours
