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Course Outline
Introduction to Mistral Medium 3
- Model architecture and capabilities
- Comparison with other Mistral models
- Key enterprise applications
Deployment Strategies
- API-based deployment
- Self-hosting with Docker and Kubernetes
- Hybrid and multi-cloud considerations
Performance Optimization
- Batching and parallelization techniques
- Model quantization and acceleration
- Cost-performance tradeoffs
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 and permissions
- Auditability and governance
Monitoring and Observability
- Tracking performance and 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 similar programming language
- Experience with machine learning model deployment
- Understanding of cloud or containerized environments
Audience
- AI/ML engineers
- Platform architects
- MLOps teams
14 Hours
