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Course Outline
Introduction to Mistral Medium 3
- Model architecture and core capabilities.
- Comparative analysis with other Mistral models.
- Key applications in enterprise scenarios.
Deployment Strategies
- API-based deployment methods.
- Self-hosting using Docker and Kubernetes.
- Considerations for hybrid and multi-cloud setups.
Performance Optimization
- Techniques for batching and parallelization.
- Model quantization and acceleration methods.
- Balancing cost and performance outcomes.
Multimodal Applications
- Integrating text and image processing capabilities.
- OCR and document intelligence solutions.
- Cross-modal enterprise workflows.
Security and Compliance
- Data residency and privacy requirements.
- Role-based access control and permissions.
- Auditability and governance frameworks.
Monitoring and Observability
- Tracking performance metrics and model drift.
- Logging and metrics pipeline management.
- Alerting mechanisms and troubleshooting.
Scaling for Enterprise
- Horizontal and vertical scaling patterns.
- Load balancing and redundancy strategies.
- Disaster recovery plans.
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 infrastructure.
Target Audience
- AI/ML engineers
- Platform architects
- MLOps teams
14 Hours
