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Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3
- Performance vs cost tradeoffs
- Enterprise-scale considerations
Deployment Patterns for LLMs
- Serving topologies and design choices
- On-premises vs cloud deployments
- Hybrid and multi-cloud strategies
Inference Optimization Techniques
- Batching strategies for high throughput
- Quantization methods for cost reduction
- Accelerator and GPU utilization
Scalability and Reliability
- Scaling Kubernetes clusters for inference
- Load balancing and traffic routing
- Fault tolerance and redundancy
Cost Engineering Frameworks
- Measuring inference cost efficiency
- Right-sizing compute and memory resources
- Monitoring and alerting for optimization
Security and Compliance in Production
- Securing deployments and APIs
- Data governance considerations
- Regulatory compliance in cost engineering
Case Studies and Best Practices
- Reference architectures for Mistral at scale
- Lessons learned from enterprise deployments
- Future trends in efficient LLM inference
Summary and Next Steps
Requirements
- Strong understanding of machine learning model deployment
- Experience with cloud infrastructure and distributed systems
- Familiarity with performance tuning and cost optimization strategies
Audience
- Infrastructure engineers
- Cloud architects
- MLOps leads
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
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