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Duration 14 hours
Course Outline
Introduction to Mistral Medium 3
- Exploring model architecture and core capabilities
- Benchmarking against other Mistral models
- Identifying key enterprise applications
Deployment Strategies
- Implementing API-based deployment workflows
- Self-hosting using Docker and Kubernetes
- Evaluating hybrid and multi-cloud deployment options
Performance Optimization
- Utilizing batching and parallelization techniques
- Applying model quantization and acceleration methods
- Balancing cost versus performance tradeoffs
Multimodal Applications
- Integrating text and image processing pipelines
- Leveraging OCR and document intelligence features
- Designing cross-modal enterprise workflows
Security and Compliance
- Addressing data residency and privacy requirements
- Configuring role-based access and permissions
- Ensuring auditability and governance compliance
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
- Configuring load balancing and redundancy
- Developing disaster recovery strategies
Summary and Next Steps
Requirements
- Strong proficiency in Python or a comparable programming language
- Practical experience in deploying machine learning models
- Solid understanding of cloud infrastructure or containerized environments
Target Audience
- AI/ML Engineers
- Platform Architects
- MLOps Teams
