Cursusaanbod

Introduction to Devstral and Mistral Models

  • Overview of Mistral’s open-source models
  • Apache-2.0 licensing and enterprise adoption
  • Devstral’s role in coding and agentic workflows

Self-Hosting Mistral and Devstral Models

  • Environment preparation and infrastructure choices
  • Containerization and deployment with Docker/Kubernetes
  • Scaling considerations for production use

Fine-Tuning Techniques

  • Supervised fine-tuning vs parameter-efficient tuning
  • Dataset preparation and cleaning
  • Domain-specific customization examples

Model Ops and Versioning

  • Best practices for model lifecycle management
  • Model versioning and rollback strategies
  • CI/CD pipelines for ML models

Governance and Compliance

  • Security considerations for open-source deployment
  • Monitoring and auditability in enterprise contexts
  • Compliance frameworks and responsible AI practices

Monitoring and Observability

  • Tracking model drift and accuracy degradation
  • Instrumentation for inference performance
  • Alerting and response workflows

Case Studies and Best Practices

  • Industry use cases of Mistral and Devstral adoption
  • Balancing cost, performance, and control
  • Lessons learned from open-source Model Ops

Summary and Next Steps

Vereisten

  • An understanding of machine learning workflows
  • Experience with Python-based ML frameworks
  • Familiarity with containerization and deployment environments

Audience

  • ML engineers
  • Data platform teams
  • Research engineers
 14 Uren

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