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Course Outline
Introduction to Mistral Medium 3
- Model architecture and capabilities.
- Comparison with other models in the Mistral lineup.
- Key enterprise applications.
Deployment Strategies
- API-based deployment approaches.
- 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 trade-offs.
Multimodal Applications
- Integrating text processing with image analysis.
- Optical Character Recognition (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 frameworks.
Monitoring and Observability
- Tracking performance metrics and model drift.
- Implementing logging and metrics pipelines.
- Alerting mechanisms and troubleshooting techniques.
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.
- Experience deploying machine learning models.
- Familiarity with cloud or containerized environments.
Target Audience
- AI/ML Engineers
- Platform Architects
- MLOps Teams
14 Hours
