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Duration 14 hours
Course Outline
Introduction to Scaling Mistral
- Deep dive into Mistral Medium 3 capabilities.
- Analyzing the tradeoffs between performance and cost.
- Key considerations for enterprise-scale deployment.
LLM Deployment Patterns
- Evaluating serving topologies and architectural design choices.
- Comparing on-premises versus cloud-based deployment models.
- Implementing hybrid and multi-cloud strategies.
Inference Optimization Techniques
- Deploying batching strategies to boost throughput.
- Applying quantization methods to drive down costs.
- Maximizing accelerator and GPU efficiency.
Scalability and Reliability
- Scaling Kubernetes clusters specifically for inference workloads.
- Implementing effective load balancing and traffic routing.
- Ensuring fault tolerance and system redundancy.
Cost Engineering Frameworks
- Assessing and measuring inference cost efficiency.
- Right-sizing compute and memory resources for optimal value.
- Setting up monitoring and alerting systems for continuous optimization.
Security and Compliance in Production
- Hardening deployments and securing API endpoints.
- Addressing data governance best practices.
- Navigating regulatory compliance within cost engineering efforts.
Case Studies and Best Practices
- Reviewing reference architectures for large-scale Mistral implementations.
- Extracting lessons from real-world enterprise deployments.
- Exploring emerging trends in efficient LLM inference.
Summary and Next Steps
Requirements
- A solid command of machine learning model deployment lifecycles.
- Practical experience with cloud infrastructure and distributed system architectures.
- Proficiency in performance tuning and cost optimization methodologies.
Target Audience
- Infrastructure Engineers
- Cloud Architects
- MLOps Leads
