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Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3.
- Balancing performance versus cost tradeoffs.
- Enterprise-scale considerations.
Deployment Patterns for LLMs
- Serving topologies and design choices.
- On-premises versus 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
- Robust 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 2600 € + VAT*
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