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Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3.
- Balancing performance and cost tradeoffs.
- Enterprise-scale considerations.
Deployment Patterns for LLMs
- Serving topologies and design choices.
- On-premises versus cloud deployments.
- Hybrid and multi-cloud strategies.
Inference Optimization Techniques
- Batching strategies for high throughput.
- Quantization methods to reduce costs.
- Utilization of accelerators and GPUs.
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 optimization.
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 scaling Mistral.
- Lessons learned from enterprise deployments.
- Future trends in efficient LLM inference.
Summary and Next Steps
Requirements
- Strong grasp of machine learning model deployment.
- Experience with cloud infrastructure and distributed systems.
- Familiarity with performance tuning and cost optimization strategies.
Audience
- Infrastructure engineers.
- Cloud architects.
- MLOps leads.
14 Hours
