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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training in SBOM for AI Artifacts training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training in SBOM for AI Artifacts training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training in SBOM for AI Artifacts training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Dive into SBOM tailored for AI artifacts. Explore CycloneDX 1.6+ standards. Analyze ML model components. Treat datasets as critical dependencies. Identify PURLs for tensors and weights. Practical cases on Hugging Face. Implement manual SBOM for AI prototypes. Best practices for naming AI artifacts. Avoid common pitfalls in ML supply chain. Workshops: Create your first simple AI SBOM. Optimize for full traceability. Enhance security of your AI projects from day 1. Master advanced metadata. Integrate open-source ML licenses.
Automate SBOM with advanced tools. Syft for dataset scanning. Ternary for ONNX models. Integrate GitHub SLSA frameworks for AI. Configure GitLab CI pipelines for AI SBOM. Concrete examples for TensorFlow SBOM. PyTorch artifacts under CycloneDX. Analyze vulnerabilities with Grype for AI. Customize schemas for fine-tuning. Practical workshops: End-to-end pipeline. Test on real Kaggle artifacts. Optimize ML scanning performance. Manage virtual environment dependencies for AI. Ensure EU AI Act compliance. Transform your DevSecOps workflows.
Analyze AI SBOM in depth. Detect poisoned dataset flaws. Model vulnerabilities via VEX. Integrate SLSA provenance for AI. Tools like Dependency-Track for AI. Custom dashboards for ML SBOM. Case study: Hugging Face supply chain attacks. Mitigate model backdoor risks. Workshops: Audit existing SBOM. Score SBOM maturity for your projects. Implement vulnerability alerting for AI. Customize ML scanning policies. NIST AI RMF compliance. Boost resilience of your AI artifacts.
Integrate SBOM into Kubernetes MLOps. Helm charts with AI SBOM. AWS SageMaker native SBOM. Azure ML provenance tools. Google Vertex AI compliance. OpenShift examples for ML SBOM. Automate via OPA policies. Workshops: Deploy SBOM-compliant AI app. Test runtime scanning. Manage secure model updates. SLSA Level 3 for AI. Real cases from Fortune 500 companies. Optimize large-scale scanning costs. Master AI cloud ecosystems.
Explore future SBOM for AI. CDXgen evolutions for ML. SPDX 3.0 for AI. Compliance with EO 14028 and EU AI Act SBOM mandates. NTIA maturity strategies. Final workshops: Capstone complete AI SBOM project. Present personal portfolio. Enterprise implementation roadmap. Best practices for governance. Evaluate SBOM ROI for AI. Network with security experts.
Target audience
Software security and AI experts. DevSecOps professionals. Experienced ML architects
Prerequisites
Mastery of SBOM standards. Knowledge of CycloneDX and SPDX. Experience in AI supply chain. Advanced knowledge of ML models
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