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Course Outline
Introduction to Mistral Medium 3
- Model architecture and core capabilities
- Comparative analysis with other Mistral models
- Prominent enterprise use cases
Deployment Approaches
- API-driven deployment
- Self-hosting via Docker and Kubernetes
- Strategies for hybrid and multi-cloud environments
Performance Optimization
- Techniques for batching and parallel processing
- Model quantization and acceleration methods
- Balancing cost and performance
Multimodal Applications
- Combining text and image processing
- OCR and document intelligence
- Cross-modal enterprise workflows
Security and Compliance
- Considerations for data residency and privacy
- Role-based access control and permissions
- Governance and auditability
Monitoring and Observability
- Tracking performance metrics and model drift
- Logging and metrics data pipelines
- Alerting mechanisms and troubleshooting
Scaling for Enterprise
- Patterns for horizontal and vertical scaling
- Load balancing and redundancy strategies
- Disaster recovery plans
Summary and Next Steps
Requirements
- Competence in Python or comparable programming languages
- Practical experience in deploying machine learning models
- Familiarity with cloud or containerized infrastructures
Target Audience
- AI/ML engineers
- Platform architects
- MLOps teams
14 Hours
