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Duration 14 hours
Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3.
- Performance versus cost trade-offs.
- Considerations for enterprise-scale operations.
Deployment Patterns for LLMs
- Serving topologies and architectural design choices.
- On-premises versus cloud deployment models.
- Hybrid and multi-cloud strategies.
Inference Optimization Techniques
- Batching strategies for high throughput.
- Quantization methods to reduce costs.
- Accelerator and GPU utilization.
Scalability and Reliability
- Scaling Kubernetes clusters for inference workloads.
- Load balancing and traffic routing.
- Fault tolerance and redundancy mechanisms.
Cost Engineering Frameworks
- Measuring inference cost efficiency.
- Right-sizing compute and memory resources.
- Monitoring and alerting for continuous optimization.
Security and Compliance in Production
- Securing deployments and APIs.
- Data governance considerations.
- Regulatory compliance within cost engineering.
Case Studies and Best Practices
- Reference architectures for scaling Mistral.
- Insights from enterprise deployments.
- Future trends in efficient LLM inference.
Summary and Next Steps
Requirements
- A solid understanding of machine learning model deployment.
- Practical experience with cloud infrastructure and distributed systems.
- Familiarity with performance tuning and cost optimization strategies.
Target Audience
- Infrastructure engineers.
- Cloud architects.
- MLOps leads.
