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Duration 14 hours
Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3 capabilities
- Balancing performance and cost tradeoffs
- Key considerations for enterprise-scale implementation
Deployment Patterns for LLMs
- Serving topologies and architectural design choices
- Comparing on-premises and cloud-based deployments
- Implementing hybrid and multi-cloud strategies
Inference Optimization Techniques
- Batching strategies to maximize throughput
- Quantization methods for reducing costs
- Optimizing accelerator and GPU utilization
Scalability and Reliability
- Scaling Kubernetes clusters for inference workloads
- Implementing load balancing and traffic routing
- Ensuring fault tolerance and system redundancy
Cost Engineering Frameworks
- Metrics for measuring inference cost efficiency
- Right-sizing compute and memory resources
- Setting up monitoring and alerting for continuous optimization
Security and Compliance in Production
- Securing deployments and API endpoints
- Addressing data governance considerations
- Meeting regulatory compliance standards in cost engineering
Case Studies and Best Practices
- Reference architectures for scaled Mistral deployments
- Insights gained from enterprise deployment experiences
- Emerging trends in efficient LLM inference
Summary and Next Steps
Requirements
- Solid understanding of machine learning model deployment processes.
- Practical experience with cloud infrastructure and distributed systems.
- Familiarity with performance tuning and cost optimization methodologies.
Audience
- Infrastructure engineers
- Cloud architects
- MLOps leads
