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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 the enterprise sector
Deployment Strategies
- API-driven deployment methods
- Self-hosting using Docker and Kubernetes
- Considerations for hybrid and multi-cloud architectures
Performance Optimization
- Techniques for batching and parallelization
- Strategies for model quantization and acceleration
- Balancing cost and performance tradeoffs
Multimodal Applications
- Integration of text and image processing workflows
- OCR and advanced document intelligence
- Designing cross-modal enterprise processes
Security and Compliance
- Data residency and privacy requirements
- Implementing role-based access and permissions
- Ensuring auditability and governance standards
Monitoring and Observability
- Tracking system performance and model drift
- Building robust logging and metrics pipelines
- Alerting mechanisms and troubleshooting techniques
Scaling for Enterprise
- Horizontal and vertical scaling patterns
- Load balancing and system redundancy
- Disaster recovery planning and strategies
Summary and Next Steps
Requirements
- Strong proficiency in Python or an equivalent programming language
- Practical experience in deploying machine learning models
- Familiarity with cloud-based or containerized operating environments
Target Audience
- AI/ML engineers
- Platform architects
- MLOps teams
14 Hours
