Reproducible AI is the practice of designing AI systems and experiments in a way that others can replicate the results. This includes sharing data, code, models, and experimental setups, thereby enhancing the credibility and scientific integrity of AI research.
Ensuring AI Reproducibility
To achieve Reproducible AI, researchers and developers should document their methodologies thoroughly, use version control for code, share datasets (while respecting privacy and ethical considerations), and provide clear instructions for replicating experiments. This transparency not only builds trust but also facilitates further innovation by allowing others to build upon existing work.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.
Several people are typing about AI/ML security. Come join us and 1000+ others in a chat that’s thoroughly SFW.