It's almost certain that your teams are pulling open source libraries down from public registries and building them into your experiments and products. This is risky. Best practice is to proxy and cache public upstreams, such as pypi.org, scan for vulnerabilities, and carry out additional checks (e.g. licensing policy checks), before allowing developers to build. With Cloudsmith, these security checks happen transparently.

By using Cloudsmith as a single source of truth for your datasets, models, containers and libraries, you can enable the reproducibility of your ML experiments. This is crucial for auditability and traceability of your work, and helps to avoid backward steps in terms of accuracy and efficiency.

Implementing robust artifact management practices today demonstrates that you're thinking ahead to real-world applications. This builds trust with investors and customers, safeguarding your team's reputation.