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London MLOps hosts our presentation
Dr Chris Monit and Phillip Henry discuss their experience bringing an ML model to production at the London MLOps meetup. Chris and Phill show how taking software engineering best practices and applying them to a machine learning project, they successfully delivered on time and on budget. They detail the problems they had and how they overcame them.
Pysynic
We release PySynic to PyPI to facilitate the use of synthetic data that can be used to test ML pipelines. Testing ML pipelines is still an immature discipline. We’re bring testing best practices to ML with a lightweight framework that helps generated synthetic data. Synthetic data is GDPR friendly and can be run in any environment. With this data, ML practitioners can write robust tests.
HSJ Awards
Our work was a finalist for the Best Healthcare Analytics Project for the NHS as nominated by the Health Service Journal. The HSJ nominated Actionable Insights, a machine learning pipeline, recognising the work on identifying ethnic and socioeconomic groups who had clinical outcomes worse than the majority.