One of the side effects of deep learning models becoming increasingly large is the amount of data that they intermediately generate which is then often ignored. Researchers have used this information to create explainability methods which show importance relative to a specific input it is connected with, but often the intermediate values are disregarded and mostly underutilized. Although this intermediate data can become increasingly unwieldily in size as the networks grow, the ability to monitor specific layers is a valuable tool that provides insight into how the model is learning as well as vague generalities about how the model performs overall (e.

Updates 1/24/23 Ran into this library cloudpickle and it reminded me of this post. 3/18/21: Found this outrun which seems to be basically a better and more thought out way to do a lot of this. Introduction Recently I became interested in better workflows for machine learning projects. One of the main issues I’ve experienced is developing locally but not being able to easily test locally. Generally this is from a local computer being a bit slower and older for the types of models and datasets I am working with and initial code decisions that resulted in poor performance on a slower computer than the server used to train.

TCAV Introduction Understanding a deep learning model is an open topic and is quite subjective. In part, I’m still unsure what exactly understanding a model means and this is brought up in many papers as understanding can be very different depending on your technical understandings. What makes a model interpretable? Some good blog posts that similarly touch on this topic are: How neural networks build up their understanding of images The building blocks of interpretability While those are great and talk about different ways of understanding models, the TCAV score is based around the idea that specific layers will be activated by features or “concepts” more than others.

fastText fastText is a useful tool that allows us to use text data and train supervised and unsupervised models. While this is possible without fastText using sklearn, spacy, etc. there are plenty of guides on those and not much information on fastText. It allows you to use it from the command line very straightforward or there is a python libary included. Part of why it’s appeling is that it is incredibly quick, straightforward and doesn’t require much knowledge to use.

Running Postgres on kubernetes locally While this may be overkill, its better than configuring a kubernetes cluster on gcloud or whatever else and if done correctly will translate to a cloud service we can later use in a production system while allowing us to focus on micro services individually. To start Kubernetes locally I am using Docker for mac which comes with Kubernetes v1.9.8 as of this time of writing and while it may not perfectly replicate a development/staging/production environment, I find it to be much more straightforward to develop in this manner due to many of the new Kubernetes tooling.