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 Duration 14 hours

Course Outline

Introduction to Mistral Medium 3

  • Model architecture and core capabilities
  • Benchmarking against other Mistral models
  • Primary enterprise use cases

Deployment Strategies

  • API-driven deployment methods
  • Self-hosting via Docker and Kubernetes
  • Hybrid and multi-cloud architectural considerations

Performance Optimization

  • Techniques for batching and parallel processing
  • Model quantization and acceleration strategies
  • Balancing cost against performance

Multimodal Applications

  • Combining text and image processing workflows
  • OCR and document intelligence integration
  • Cross-modal enterprise processes

Security and Compliance

  • Data residency and privacy safeguards
  • Role-based access control and permissions
  • Ensuring auditability and governance

Monitoring and Observability

  • Performance tracking and drift detection
  • Establishing logging and metrics pipelines
  • Alerting mechanisms and troubleshooting procedures

Scaling for Enterprise

  • Horizontal and vertical scaling patterns
  • Load balancing and system redundancy
  • Disaster recovery planning

Summary and Next Steps

Requirements

  • Proficiency in Python or a comparable programming language.
  • Practical experience with machine learning model deployment.
  • Familiarity with cloud or containerized infrastructure.

Target Audience

  • AI/ML Engineers
  • Platform Architects
  • MLOps Teams

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