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Duration 14 hours
Course Outline
Introduction to Mistral Medium 3
- Overview of model architecture and core capabilities
- Benchmarking against other Mistral model variants
- Identifying key enterprise use cases
Deployment Strategies
- Implementing API-based integration
- Self-hosting solutions using Docker and Kubernetes
- Navigating hybrid and multi-cloud deployment landscapes
Performance Optimization
- Utilising batching and parallelisation methods
- Applying model quantisation and acceleration techniques
- Balancing cost against performance requirements
Multimodal Applications
- Fusing text and image processing pipelines
- Leveraging OCR and document intelligence tools
- Designing cross-modal enterprise workflows
Security and Compliance
- Addressing data residency and privacy regulations
- Configuring role-based access controls and permissions
- Ensuring auditability and governance compliance
Monitoring and Observability
- Monitoring performance metrics and model drift
- Establishing logging and metrics pipelines
- Setting up alerting systems and troubleshooting protocols
Scaling for Enterprise
- Adopting horizontal and vertical scaling patterns
- Implementing load balancing and high-availability measures
- Formulating disaster recovery strategies
Summary and Next Steps
Requirements
- Strong proficiency in Python or equivalent programming languages
- Hands-on experience with deploying machine learning models
- Familiarity with cloud-based or containerised infrastructures
Intended Audience
- AI/ML Engineers
- Platform Architects
- MLOps Teams
