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Course Outline

Getting Started with Mistral Medium 3

  • Examining model architecture and core capabilities
  • Benchmarking against other Mistral models
  • Identifying key enterprise use cases

Deployment Approaches

  • Utilizing API-driven deployment methods
  • Self-hosting using Docker and Kubernetes
  • Navigating hybrid and multi-cloud strategies

Optimizing Performance

  • Applying batching and parallel processing techniques
  • Leveraging model quantization and speed enhancements
  • Balancing cost against performance metrics

Multimodal Implementations

  • Merging text and image processing pipelines
  • Implementing OCR and document intelligence features
  • Designing cross-modal enterprise workflows

Ensuring Security and Compliance

  • Addressing data residency and privacy requirements
  • Configuring role-based access controls and permissions
  • Establishing audit trails and governance frameworks

Monitoring and Observability

  • Maintaining performance metrics and detecting drift
  • Building robust logging and metrics pipelines
  • Setting up alerts and troubleshooting mechanisms

Enterprise Scaling

  • Implementing horizontal and vertical scaling patterns
  • Managing load balancing and system redundancy
  • Developing comprehensive disaster recovery plans

Wrap-up and Future Directions

Requirements

  • Strong proficiency in Python or a comparable programming language
  • Practical experience in deploying machine learning models
  • Familiarity with cloud-based or containerized infrastructure

Intended Audience

  • AI/ML Engineers
  • Platform Architects
  • MLOps Teams
 14 Hours

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