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Duration 14 hours
Course Outline
Introduction to Mistral Medium 3
- Architecture details and core capabilities
- Benchmarking against other models in the Mistral series
- Primary enterprise use cases
Deployment Strategies
- API-centric deployment methods
- Self-hosting solutions using Docker and Kubernetes
- Navigating hybrid and multi-cloud architectures
Performance Optimization
- Strategies for batching and parallel processing
- Model quantization and acceleration techniques
- Balancing cost efficiency with performance
Multimodal Applications
- Combining text and image processing workflows
- OCR and advanced document intelligence
- Designing cross-modal enterprise solutions
Security and Compliance
- Data residency and privacy safeguarding
- Configuring role-based access and permissions
- Ensuring auditability and governance standards
Monitoring and Observability
- Monitoring performance metrics and model drift
- Constructing robust logging and metrics pipelines
- Setting up alerting mechanisms and troubleshooting procedures
Enterprise Scaling
- Horizontal and vertical scaling patterns
- Implementing load balancing and system redundancy
- Formulating disaster recovery plans
Summary and Next Steps
Requirements
- Strong proficiency in Python or an equivalent programming language
- Practical experience in deploying machine learning models
- Familiarity with cloud or containerized infrastructure
Target Audience
- AI/ML engineers
- Platform architects
- MLOps professionals
