Open-Source Model Ops: Self-Hosting, Fine-Tuning and Governance with Devstral & Mistral Models Training Course
Devstral and Mistral models are open-source AI technologies designed for flexible deployment, fine-tuning, and scalable integration.
This instructor-led, live training (online or onsite) is aimed at intermediate–level to advanced–level ML engineers, platform teams, and research engineers who wish to self-host, fine-tune, and govern Mistral and Devstral models in production environments.
By the end of this training, participants will be able to:
- Set up and configure self-hosted environments for Mistral and Devstral models.
- Apply fine-tuning techniques for domain-specific performance.
- Implement versioning, monitoring, and lifecycle governance.
- Ensure security, compliance, and responsible usage of open-source models.
Format of the Course
- Interactive lecture and discussion.
- Hands-on exercises in self-hosting and fine-tuning.
- Live-lab implementation of governance and monitoring pipelines.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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Course Outline
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
Requirements
- 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 Hours
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