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Duration 14 hours
Course Outline
Introduction to Mistral Medium 3
- Examining model architecture and core capabilities
- Benchmarking against other models in the Mistral family
- Identifying key enterprise use cases
Deployment Approaches
- Setting up API-based connections
- Self-hosting solutions using Docker and Kubernetes
- Evaluating hybrid and multi-cloud strategies
Optimizing Performance
- Utilizing batching and parallel processing techniques
- Applying model quantization for speed improvements
- Balancing cost efficiency with performance metrics
Multimodal Use Cases
- Combining text analysis with image processing
- Leveraging OCR and document intelligence features
- Designing workflows that span multiple modalities
Security and Compliance Frameworks
- Addressing data residency and privacy regulations
- Configuring role-based access controls and permissions
- Establishing audit trails and governance standards
Monitoring and Observability
- Tracking system performance and detecting drift
- Building robust logging and metrics pipelines
- Implementing alerting systems and troubleshooting protocols
Enterprise-Scale Scaling
- Adopting horizontal and vertical scaling patterns
- Configuring load balancing and redundancy layers
- Developing disaster recovery plans
Summary and Future Pathways
Requirements
- Solid command of Python or comparable programming languages
- Practical experience in deploying machine learning models
- Familiarity with cloud infrastructures or containerized environments
Target Audience
- AI/ML Engineers
- Platform Architects
- MLOps Teams
