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Duration 14 hours
Course Outline
Foundations of Mistral at Scale
- Key features of Mistral Medium 3
- Balancing performance against cost
- Considerations for enterprise-scale operations
Strategies for LLM Deployment
- Selecting serving topologies and design approaches
- Comparing on-premises and cloud-based deployments
- Implementing hybrid and multi-cloud frameworks
Techniques for Optimizing Inference
- Employing batching for high throughput
- Using quantization to reduce costs
- Maximizing accelerator and GPU efficiency
Ensuring Scalability and Reliability
- Scaling Kubernetes clusters for inference tasks
- Managing load balancing and traffic distribution
- Building fault tolerance and redundancy
Frameworks for Cost Engineering
- Evaluating inference cost efficiency
- Right-sizing compute and memory allocations
- Setting up monitoring and alerts for continuous optimization
Production Security and Compliance
- Protecting deployments and API endpoints
- Addressing data governance needs
- Ensuring regulatory compliance within cost engineering
Real-World Cases and Industry Best Practices
- Reference architectures for large-scale Mistral usage
- Insights from enterprise implementation experiences
- Emerging trends in efficient LLM inference
Conclusions and Path Forward
Requirements
- A robust grasp of machine learning model deployment processes.
- Practical experience with cloud infrastructure and distributed systems.
- Working knowledge of performance tuning and cost optimization methodologies.
Target Audience
- Infrastructure engineers
- Cloud architects
- MLOps leads
