Azure Machine Learning "managed endpoints" and "CLI v 2" are huge advances for the platform. If you're new to AzML or have been frustrated in the past, I urge you to revisit and focus on these features.

In an ML model for ID'ing bird calls, you _don't_ want to slice the training data into small, eg, 5 sec slices. All bird recordings are polluted w background, non-target birds. You need to multimodel, w 1st model grabbing long, eg, 60 secs saying 'ROI for target species at seconds :31-:36" and then your 2nd model is your fine-tuned discriminator.

How mechanical watches work. Even if you're not interested, go to this page and look at the incredible interactive demos.


"Self-portrait of a transformer-based image-generating machine learning model, in the style of John Singer Sargent" -e

Eager to try Py-Script — easily embedded directly into HTML.

Wish I had had time for this Kaggle competition for ID’ing native Hawaiian birds from their songs. Fun tidbit: a leading way to do sound ID is to convert it to a spectrogram and then just do image recognition on it!

Fascinating article on building an model for the _te reo_ Māori language, using a license specifically geared towards the indigenous culture.

Does the "Federated" timeline adjust to show you languages you know/interact with? Almost 33% of the posts I see I can't read. Will that change over time?

Recently retired from Microsoft (Azure ML). Career-long interests in: prog langs, psychology of s/w dev, machine-driven sol’ns (dynamic programming, ML, etc.), scheduling/bin-packing niche. Used to edit mags: Computer Language, Game Developer, AI Expert. Retirement focus is ML for environment.

Most common tools: Python (I wish F#), PyTorch, Fast.AI.

Live in Hawai’i (Big Island), lot of time on/underwater.


Extremely nerdy.

This is a really wonderful explanation of transformers (the # mlpfim architecture behind the boom in ) for software engineers. For me, vastly clearer than other “visual” explanations.


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