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Duration 14 hours
Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3 capabilities
- Trade-offs between performance and cost
- Key considerations for enterprise-scale operations
Deployment Patterns for LLMs
- Choosing serving topologies and design options
- Comparing on-premises and cloud deployment models
- Implementing hybrid and multi-cloud strategies
Inference Optimization Techniques
- Strategies for high-throughput batching
- Quantization methods to lower costs
- Maximizing accelerator and GPU utilization
Scalability and Reliability
- Scaling Kubernetes clusters for inference workloads
- Implementing load balancing and traffic routing
- Ensuring fault tolerance and redundancy
Cost Engineering Frameworks
- Measuring inference cost efficiency
- Right-sizing compute and memory resources
- Setting up monitoring and alerting for continuous optimization
Security and Compliance in Production
- Securing deployment environments and APIs
- Data governance best practices
- Ensuring regulatory compliance within cost engineering frameworks
Case Studies and Best Practices
- Reference architectures for scaling Mistral
- Insights from enterprise deployment experiences
- Emerging trends in efficient LLM inference
Summary and Next Steps
Requirements
- Comprehensive knowledge of machine learning model deployment workflows
- Practical experience with cloud infrastructure and distributed systems
- Proficiency in performance tuning and cost optimization techniques
Intended Audience
- Infrastructure Engineers
- Cloud Architects
- MLOps Leads
