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Duration 14 hours
Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3 capabilities
- Analyzing performance versus cost tradeoffs
- Key considerations for enterprise-scale deployment
Deployment Patterns for LLMs
- Serving topologies and architectural decisions
- On-premises versus cloud deployment models
- Implementing hybrid and multi-cloud strategies
Inference Optimization Techniques
- Batching strategies to enhance high throughput
- Quantization methods for significant cost reduction
- Maximizing 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
- Measuring inference cost efficiency
- Right-sizing compute and memory resources
- Monitoring and alerting for continuous optimization
Security and Compliance in Production
- Securing production deployments and APIs
- Navigating data governance requirements
- Adhering to regulatory standards in cost engineering
Case Studies and Best Practices
- Reference architectures for large-scale Mistral implementation
- Key insights from enterprise deployment experiences
- Emerging trends in efficient LLM inference
Summary and Next Steps
Requirements
- A solid grasp of machine learning model deployment principles
- Practical experience with cloud infrastructure and distributed systems
- Proficiency in performance tuning and cost optimization methodologies
Target Audience
- Infrastructure engineers
- Cloud architects
- MLOps leads
