Dream Cognitive Load – Minimum Viable Python

Early this morning, I saw several white Queen Anne’s lace flowers (aka wild carrot) waving in the breeze, with two or three pink chicory flowers nearby. Since wild chicory flowers are blue, I grabbed my phone and moved to photograph the pink flowers with a background of white compound flowers.

In flower photography, flowers are a great-looking background for other flowers.

Then, as if to answer the question “do you dream in color?” I woke up.

(speaking of cognition…)

Emily Robinson’s recent talk on machine learning at the SoCal R users group SoCal RUG was a good example of how to reduce cognitive load, (for me, anyway!) It was also super fun to go to a meetup, expecting it to just be about machine learning on AWS, and then find that it was being given by the author of a book on how to build your career in data science

The talk reduced cognitive load because it was not about how to produce the BEST predictive model of dog image ratings AND how to use AWS for machine learning. It was ONLY about how to do it on AWS using Amazon Sagemaker.

A relaxing aspect was that @robinson_es put up a link to her slides. You could go back a slide, or see the main points of a slide just before she was about to reveal the next line. No tension!

Emily Robinson slides

In the Q and A, someone typed into the chat, asking if you could run SageMaker locally. There wasn’t time to go into that, but it looks like you can! You put your images out on AWS, but run locally. Here’s the whole posting:

Amazon SageMaker Local Mode

So, here’s your ‘minimum viable python’ moment :-)

pip install -U sagemaker

And, after that,

“Just make sure you have the latest version of the SageMaker Python SDK, install a few other tools, and change one line of code!”<<

If that isn’t MVP, I don’t know what is.

(speaking of cognitive load…)

Here is an upcoming discussion at a different meetup – Tues October 18 – on a topic taken from one of the ThoughtWorks radars – on team cognitive load

--- title: "Dream Cognitive Load" author: "George Girton" date: "2022-10-03" categories: [machine learning, python] image: "QueenAnnesLace-0808_Web.png" draft: false --- Early this morning, I saw several white Queen Anne's lace flowers (aka wild carrot) waving in the breeze, with two or three pink chicory flowers nearby. Since wild chicory flowers are blue, I grabbed my phone and moved to photograph the pink flowers with a background of white compound flowers. [In flower photography, flowers are a great-looking background for other flowers.]{.aside} Then, as if to answer the question "do you dream in color?" I woke up. ------------------------------------------------------------------------ (speaking of cognition...) Emily Robinson's recent talk on machine learning at the SoCal R users group [SoCal RUG](https://www.meetup.com/SOCAL-RUG/) was a good example of how to reduce cognitive load, (for me, anyway!) It was also super fun to go to a meetup, expecting it to just be about machine learning on AWS, and then find that it was being given by the author of a [book on how to build your career in data science](https://hookedondata.org/about/) The talk reduced cognitive load because it was not about how to produce the BEST predictive model of dog image ratings AND how to use AWS for machine learning. It was ONLY about how to do it on AWS using Amazon Sagemaker. A relaxing aspect was that @robinson_es put up a link to her slides. You could go back a slide, or see the main points of a slide just before she was about to reveal the next line. No tension! [Emily Robinson slides](https://docs.google.com/presentation/d/1po0xPOujqzrfFZgYrMEhWcY68nVagZQk/edit#slide=id.p51) In the Q and A, someone typed into the chat, asking if you could run SageMaker locally. There wasn't time to go into that, but it looks like you can! You put your images out on AWS, but run locally. Here's the whole posting: [Amazon SageMaker Local Mode](https://aws.amazon.com/blogs/machine-learning/use-the-amazon-sagemaker-local-mode-to-train-on-your-notebook-instance/) So, here's your 'minimum viable python' moment :-) pip install -U sagemaker And, after that, > > "Just make sure you have the latest version of the SageMaker Python SDK, install a few other tools, and change one line of code!"\<\< If that isn't MVP, I don't know what is. ![false-color pink Chicory](PinkChicory_IMG_7892.png) (speaking of cognitive load...) Here is an upcoming discussion at a different meetup -- Tues October 18 -- on a topic taken from one of the ThoughtWorks radars -- on [team cognitive load](https://www.meetup.com/lajugstudygroup/events/286770806/)