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Course Outline
Introduction to Mistral at Scale for Government
- Overview of Mistral Medium 3 for government applications
- Performance versus cost tradeoffs in a public sector context
- Enterprise-scale considerations for government agencies
Deployment Patterns for LLMs in Government
- Serving topologies and design choices for government systems
- On-premises versus cloud deployments for government operations
- Hybrid and multi-cloud strategies for enhanced flexibility and security
Inference Optimization Techniques for Government
- Batching strategies to achieve high throughput in government environments
- Quantization methods to reduce costs while maintaining performance for government tasks
- Optimizing accelerator and GPU utilization for government workloads
Scalability and Reliability for Government Operations
- Scaling Kubernetes clusters to support inference in government systems
- Load balancing and traffic routing to ensure reliable service delivery for government applications
- Fault tolerance and redundancy to maintain continuous operations for government services
Cost Engineering Frameworks for Government
- Measuring inference cost efficiency in government projects
- Right-sizing compute and memory resources to optimize costs for government initiatives
- Monitoring and alerting systems for ongoing optimization in government workflows
Security and Compliance in Government Production Environments
- Securing deployments and APIs to meet government security standards
- Data governance considerations to ensure compliance with government regulations
- Regulatory compliance in cost engineering for government projects
Case Studies and Best Practices for Government Use Cases
- Reference architectures for deploying Mistral at scale in government agencies
- Lessons learned from enterprise deployments within the public sector
- Future trends in efficient LLM inference for government applications
Summary and Next Steps for Government Agencies
Requirements
- Strong understanding of machine learning model deployment for government applications
- Experience with cloud infrastructure and distributed systems in a public sector environment
- Familiarity with performance tuning and cost optimization strategies aligned with government budgets
Audience
- Infrastructure engineers for government agencies
- Cloud architects for government projects
- MLOps leads for government initiatives
14 Hours
