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Duration 14 hours
Course Outline
Overview of Mistral Medium 3
- Model architecture and core capabilities
- Benchmarking against other Mistral models
- Primary enterprise use cases
Deployment Methodologies
- API-based deployment strategies
- Self-hosting using Docker and Kubernetes
- Navigating hybrid and multi-cloud environments
Performance Tuning
- Techniques for batching and parallelization
- Model quantization and speed optimization
- Balancing cost versus performance
Multimodal Integrations
- Combining text and image processing workflows
- OCR and advanced document intelligence
- Cross-modal enterprise process automation
Security and Regulatory Compliance
- Data residency and privacy protocols
- Managing role-based access and permissions
- Ensuring auditability and governance
Monitoring and Observability
- Tracking system performance and model drift
- Constructing logging and metrics pipelines
- Implementing alerting and debugging workflows
Enterprise-Scale Scaling
- Patterns for horizontal and vertical scaling
- Load balancing and redundancy planning
- Disaster recovery protocols
Conclusion and Future Pathways
Requirements
- Strong proficiency in Python or equivalent programming languages
- Practical experience in deploying machine learning models
- Familiarity with cloud or containerized environments
Target Audience
- AI/ML Engineers
- Platform Architects
- MLOps Teams
