Hey everyone,
I’m curious to learn how teams in biotech or AI-focused research environments are leveraging J i r a to manage their machine learning and data science workflows.
Biotech companies (which build AI for precision medicine and immunology) must deal with a lot of complex, cross-functional tasks — data Pre-processing, model training, experimentation, validation, documentation, and deployment — all while complying with regulatory standards.
So my questions are:
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How do you structure your J i r a boards for such technical workflows?
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Do you use custom issue types or stick with Epics/Stories/Tasks?
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Are there any automation rules you’ve found especially useful for AI/ML projects?
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How do you integrate J i r a with tools like Bit-bucket, Confluence, or ML-flow, if at all?
I’m trying to streamline project management for a small team working on AI-driven research, and would love to hear best practices, templates, or lessons learned.
Thanks in advance! 🚀
