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Course Outline
Introduction to Mistral Medium 3
- Model architecture and core capabilities
- Positioning within the broader Mistral model family
- Primary enterprise application scenarios
Deployment Strategies
- API-integrated deployment methods
- Self-hosting using Docker and Kubernetes
- Hybrid and multi-cloud architectural considerations
Performance Optimization
- Batching and parallelization strategies
- Model quantization and inference acceleration
- Balancing cost against performance
Multimodal Applications
- Integrating text and image processing pipelines
- OCR and advanced document intelligence
- Designing cross-modal enterprise workflows
Security and Compliance
- Data residency and privacy regulations
- Role-based access control and permission management
- Ensuring auditability and governance
Monitoring and Observability
- Monitoring performance metrics and model drift
- Implementing logging and metrics pipelines
- Configuring alerts and troubleshooting mechanisms
Scaling for Enterprise
- Horizontal and vertical scaling patterns
- Load balancing and redundancy design
- Disaster recovery planning
Summary and Next Steps
Requirements
- Strong proficiency in Python or comparable programming languages
- Practical experience in deploying machine learning models
- Familiarity with cloud or containerized infrastructure
Target Audience
- AI/ML Engineers
- Platform Architects
- MLOps Teams
14 Hours
