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Duration 14 hours
Course Outline
Introduction to Mistral at Scale
- Key features of Mistral Medium 3
- Balancing performance against cost
- Considerations for enterprise-scale implementation
Deployment Patterns for LLMs
- Serving topologies and architectural choices
- Comparing on-premises and cloud-based deployments
- Hybrid and multi-cloud implementation strategies
Inference Optimization Techniques
- Batching strategies to maximize throughput
- Quantization methods to lower costs
- Optimizing accelerator and GPU usage
Scalability and Reliability
- Scaling Kubernetes clusters for inference workloads
- Implementing load balancing and traffic management
- Ensuring fault tolerance and system redundancy
Cost Engineering Frameworks
- Evaluating inference cost efficiency
- Right-sizing compute and memory allocations
- Setting up monitoring and alerting for continuous optimization
Security and Compliance in Production
- Hardening deployments and API security
- Data governance best practices
- Maintaining regulatory compliance in cost engineering
Case Studies and Best Practices
- Reference architectures for large-scale Mistral deployments
- Insights from enterprise implementation experiences
- Emerging trends in efficient LLM inference
Summary and Next Steps
Requirements
- Proficient knowledge of machine learning model deployment
- Hands-on experience with cloud infrastructure and distributed systems
- Working knowledge of performance tuning and cost optimization techniques
Target Audience
- Infrastructure engineers
- Cloud architects
- MLOps leads
