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Duration 14 hours
Course Outline
Introduction to Mistral Medium 3
- Exploring model architecture and capabilities
- Benchmarking against other Mistral models
- Identifying key enterprise applications
Deployment Strategies
- Implementing API-based deployments
- Self-hosting using Docker and Kubernetes
- Navigating hybrid and multi-cloud complexities
Performance Optimization
- Applying batching and parallelization techniques
- Leveraging model quantization and acceleration
- Balancing cost versus performance tradeoffs
Multimodal Applications
- Fusion of text and image processing
- Utilizing OCR and document intelligence
- Designing cross-modal enterprise workflows
Security and Compliance
- Addressing data residency and privacy requirements
- Managing role-based access and permissions
- Ensuring auditability and governance
Monitoring and Observability
- Monitoring performance and detecting drift
- Establishing logging and metrics pipelines
- Configuring alerting and troubleshooting mechanisms
Scaling for Enterprise
- Designing horizontal and vertical scaling patterns
- Implementing load balancing and redundancy
- Formulating disaster recovery strategies
Summary and Next Steps
Requirements
- Solid proficiency in Python or comparable programming languages
- Practical experience in deploying machine learning models
- Familiarity with cloud infrastructure or containerized environments
Target Audience
- AI/ML Engineers
- Platform Architects
- MLOps Professionals
