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Duration 14 hours
Course Outline
Introduction to Mistral at Scale
- An overview of Mistral Medium 3
- Trade-offs between performance and cost
- Considerations for enterprise-scale operations
Deployment Patterns for Large Language Models
- Serving topologies and architectural decisions
- On-premises versus cloud-based deployments
- 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 tasks
- Load balancing and traffic routing mechanisms
- Fault tolerance and redundancy measures
Cost Engineering Frameworks
- Assessing inference cost efficiency
- Right-sizing compute and memory resources
- Monitoring and alerting for continuous optimization
Security and Compliance in Production
- Securing deployments and API endpoints
- Data governance considerations
- Regulatory compliance within cost engineering
Case Studies and Best Practices
- Reference architectures for Mistral at scale
- Insights gained from enterprise deployments
- Emerging trends in efficient LLM inference
Summary and Next Steps
Requirements
- A solid grasp of machine learning model deployment concepts.
- Practical experience with cloud infrastructure and distributed systems.
- Knowledge of performance tuning and cost optimization methodologies.
Target Audience
- Infrastructure Engineers
- Cloud Architects
- MLOps Leads
