A high level introduction to Dask
How we built Omigami: A scalable machine learning tool for metabolomic researchers
Leverage machine learning to detect poor quality integrations and save hours assessing peaks manually
Grokking the internals of Dask
Predict structure similarity from MS/MS spectra directly with a deep learning model.
What it’s like to be part of our team and why it might be different from what you expect
Properly tracking your machine learning experiments is easier than you think.
A new tool to smartly enrich molecular networks with metabolite annotations from different sources
Spec2Vec uses an unsupervised machine learning model to predict structural similarity from MS/MS mass spectra.
FEAST is the only standalone open-source feature store, but you have some other options too.
It’s Open Source, plays nicely with Kubernetes, and there’s a great community
A novel approach to feature annotation
Why we abandoned Kubeflow in our machine learning architecture
Insights from Nathan Wan
How to not overfit your models and get less false positives
Comparing machine learning platforms
Comparing data dashboarding tools and frameworks
Choosing a task orchestration tool
How can you process more data quicker?
How to deal with unseen data, optimise response times, and update models frequently.
How to parallelize and distribute your Python machine learning pipelines with Luigi, Docker, and Kubernetes
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