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Duration 14 hours
Course Outline
Overview of Mistral Medium 3
- Model architecture and feature set
- Positioning relative to other Mistral models
- Primary enterprise use cases
Deployment Approaches
- Implementing via API integration
- Self-hosting using Docker and Kubernetes
- Navigating hybrid and multi-cloud environments
Optimising Performance
- Strategies for batching and parallel processing
- Applying model quantization and acceleration
- Balancing cost against performance
Multimodal Implementations
- Combining text and image processing workflows
- Utilising OCR and document intelligence
- Designing cross-modal enterprise processes
Security and Regulatory Compliance
- Addressing data residency and privacy mandates
- Configuring role-based access controls
- Ensuring audit trails and governance
Monitoring and Insight
- Monitoring performance metrics and model drift
- Building robust logging and metrics pipelines
- Setting up alerting systems and troubleshooting
Enterprise Scalability
- Horizontal and vertical scaling patterns
- Implementing load balancing and redundancy
- Planning disaster recovery measures
Wrap-up and Future Directions
Requirements
- Strong command of Python or a comparable programming language
- Practical experience deploying machine learning models
- Familiarity with cloud or containerised infrastructures
Target Audience
- AI/ML engineers
- Platform architects
- MLOps specialists
