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Duration 14 hours
Course Outline
Introduction to Mistral Medium 3
- Exploration of model architecture and core capabilities
- Benchmarking against other models in the Mistral lineup
- Identification of key enterprise applications
Deployment Strategies
- Implementing API-based integration methods
- Self-hosting solutions using Docker and Kubernetes
- Considerations for hybrid and multi-cloud architectures
Performance Optimization
- Techniques for batching and parallel processing
- Strategies for model quantization and acceleration
- Balancing cost-efficiency with performance requirements
Multimodal Applications
- Combining text and image processing capabilities
- Leveraging OCR and document intelligence tools
- Designing cross-modal enterprise workflows
Security and Compliance
- Addressing data residency and privacy mandates
- Implementing role-based access controls and permissions
- Ensuring auditability and governance structures
Monitoring and Observability
- Monitoring system performance and data drift
- Establishing robust logging and metrics pipelines
- Configuring alerting systems and troubleshooting mechanisms
Scaling for Enterprise
- Patterns for horizontal and vertical scaling
- Configuring load balancing and system redundancy
- Developing comprehensive disaster recovery strategies
Summary and Next Steps
Requirements
- Strong proficiency in Python or an equivalent programming language
- Practical experience with deploying machine learning models
- Familiarity with cloud infrastructure or containerized environments
Target Audience
- AI/ML Engineers
- Platform Architects
- MLOps Teams
