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Duration 14 hours
Course Outline
Introduction to Mistral Medium 3
- Exploration of model architecture and core capabilities
- Benchmarking against other Mistral models
- Identification of key enterprise applications
Deployment Strategies
- Implementing API-based deployment
- Self-hosting using Docker and Kubernetes
- Navigating hybrid and multi-cloud environments
Performance Optimization
- Utilizing batching and parallelization techniques
- Applying model quantization and acceleration methods
- Balancing cost against performance metrics
Multimodal Applications
- Integrating text and image processing workflows
- Leveraging OCR and document intelligence
- Designing cross-modal enterprise workflows
Security and Compliance
- Addressing data residency and privacy concerns
- Configuring role-based access and permissions
- Ensuring auditability and governance
Monitoring and Observability
- Tracking system performance and model drift
- Establishing logging and metrics pipelines
- Implementing alerting systems and troubleshooting procedures
Scaling for Enterprise
- Applying horizontal and vertical scaling patterns
- Implementing load balancing and redundancy
- Formulating disaster recovery strategies
Summary and Next Steps
Requirements
- Strong proficiency in Python or a comparable programming language
- Practical experience in deploying machine learning models
- Familiarity with cloud infrastructure or containerized environments
Target Audience
- AI/ML engineers
- Platform architects
- MLOps teams
