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Course Outline
Introduction to Mistral Medium 3
- Model architecture and key capabilities.
- Comparison with other Mistral models.
- Primary enterprise use cases.
Deployment Strategies
- API-based deployment.
- Self-hosting using Docker and Kubernetes.
- Hybrid and multi-cloud considerations.
Performance Optimization
- Techniques for batching and parallelization.
- Model quantization and acceleration.
- Balancing cost and performance.
Multimodal Applications
- Integrating text and image processing.
- OCR and document intelligence.
- Cross-modal enterprise workflows.
Security and Compliance
- Data residency and privacy considerations.
- Role-based access control and permissions.
- Auditability and governance.
Monitoring and Observability
- Tracking performance and model drift.
- Logging and metrics pipelines.
- Alerting and troubleshooting.
Scaling for Enterprise
- Horizontal and vertical scaling patterns.
- Load balancing and redundancy.
- Disaster recovery strategies.
Summary and Next Steps
Requirements
- Proficiency in Python or a comparable programming language.
- Experience deploying machine learning models.
- Familiarity with cloud or containerized environments.
Target Audience
- AI/ML engineers.
- Platform architects.
- MLOps teams.
14 Hours
