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Duration 14 hours
Course Outline
Introduction to Mistral Medium 3
- Model architecture and core capabilities
- Benchmarking against other Mistral models
- Key enterprise application scenarios
Deployment Strategies
- API-based deployment approaches
- Self-hosting solutions using Docker and Kubernetes
- Hybrid and multi-cloud architectural considerations
Performance Optimization
- Techniques for batching and parallelization
- Model quantization and acceleration methods
- Balancing cost versus performance
Multimodal Applications
- Integrating text and image processing capabilities
- OCR and document intelligence solutions
- Building cross-modal enterprise workflows
Security and Compliance
- Data residency and privacy regulatory considerations
- Implementing role-based access control and permissions
- Ensuring auditability and governance standards
Monitoring and Observability
- Tracking system performance and model drift
- Establishing logging and metrics pipelines
- Configuring alerting and troubleshooting mechanisms
Scaling for Enterprise
- Horizontal and vertical scaling patterns
- Load balancing and system redundancy
- Disaster recovery strategy planning
Summary and Next Steps
Requirements
- Proficiency in Python or a comparable programming language
- Practical experience in deploying machine learning models
- Familiarity with cloud or containerized environments
Target Audience
- AI/ML Engineers
- Platform Architects
- MLOps Teams
