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Duration 14 hours
Course Outline
Introduction to Mistral at Scale
- Overview of Mistral Medium 3
- Performance vs. cost trade-offs
- Considerations for enterprise-scale operations
Deployment Patterns for LLMs
- Serving topologies and architectural decisions
- On-premises vs. cloud-based deployments
- Hybrid and multi-cloud strategies
Inference Optimisation Techniques
- Batching strategies for high throughput
- Quantisation methods for cost reduction
- Accelerator and GPU utilisation
Scalability and Reliability
- Scaling Kubernetes clusters for inference workloads
- Load balancing and traffic routing
- Fault tolerance and redundancy mechanisms
Cost Engineering Frameworks
- Evaluating inference cost efficiency
- Right-sizing compute and memory resources
- Monitoring and alerting for continuous optimisation
Security and Compliance in Production
- Securing deployments and API endpoints
- Data governance considerations
- Regulatory compliance within 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
- Proficiency in deploying machine learning models.
- Hands-on experience with cloud infrastructure and distributed systems.
- Knowledge of performance tuning and cost optimisation strategies.
Audience
- Infrastructure Engineers
- Cloud Architects
- MLOps Leads
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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