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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 series
- Identification of key enterprise application scenarios
Deployment Strategies
- Implementing API-based deployment workflows
- Self-hosting using Docker and Kubernetes
- Navigating hybrid and multi-cloud architectural considerations
Performance Optimization
- Applying batching and parallelization methods
- Leveraging model quantization and acceleration techniques
- Balancing cost against performance requirements
Multimodal Applications
- Integrating text and image processing pipelines
- Implementing OCR and document intelligence features
- Designing cross-modal workflows for enterprise operations
Security and Compliance
- Addressing data residency and privacy standards
- Configuring role-based access control and permissions
- Ensuring auditability and organizational governance
Monitoring and Observability
- Tracking system performance and model drift
- Establishing logging and metrics pipelines
- Setting up alerting systems and troubleshooting protocols
Scaling for Enterprise
- Implementing horizontal and vertical scaling patterns
- Managing load balancing and system redundancy
- Developing disaster recovery strategies
Summary and Next Steps
Requirements
- Proficiency in Python or a comparable programming language
- Practical experience in deploying machine learning models
- Working knowledge of cloud or containerized infrastructure environments
Target Audience
- AI/ML Engineers
- Platform Architects
- MLOps Teams
