- Metadata, Medical Data and TF.Data
- 🩺 AI in Medicine
- 📼 Metadata
- 🖇 Tensorflow 2.3
Metadata, Medical Data and TF.Data
This week turned out to be about data, can you imagine? There were announcements in the metadata management from Shopify and Stripe. What it takes to build a startup in the field of medical AI and how much time you’ll spend gathering data for it. And TensorFlow’s latest release was mostly about its data API.
🩺 AI in Medicine
Highly recommend the Data Futurology podcast about what it takes to build an AI company in the medical sphere. Many interesting things, but what it takes to build real-world datasets in the wild is always worth hearing:
- “There is a lot of differences in medical data — if you did MRIs in two different centers, you cannot just take data from both of them and use it.”
- “Another under-appreciated aspect of building a lot of real-world AI applications, where, unlike kaggle, nobody’s got a 100 thousand in a nicely organized folder… Sometimes only having data for 10 patients at a time, scans coming on CDs, 1 at a time.”
“As much as our system involves AI and image processing there is probably just as much if not more work in around data standardization, data cleanliness and manual intervention into data.”
- 2.5 years (from 5!) were spent on building a political relationship (with doctors), gathering data piece by piece, later building integrations with existing systems.
- “The best results were coming from building a relationship with individual doctors.”
To sum it up, I think that data gathering relationship building is the new sales. Building a company that relies on data, you are as good as the number of data providers you’ve built a relationship with.
Two of the big players have released something about their metadata solutions. Many of the big players already have established solutions for a couple of years, with Shopify being the latest company to build their own.
- Their implementation uses Elasticseach and a graph database to provide search and data lineage respectively. GraphQL’s Apollo as an API layer. Quite a standard stack. Similar to e.g. this one.
- Other than that, from the screenshots it looks like it does what it should and looks very much like similar systems. However, a quote from the article explaining why it’s generally hard to reuse existing solutions:
Every organization’s data stack is different. While some upstream processes can be standardized and cataloged appropriately, the business context of downstream processes creates a wide distribution of requirements that are near impossible to satisfy with a one-size-fits-all solution.
Stripe and Privacy
A collection of data discovery articles.
🖇 Tensorflow 2.3
Ironically the latest TensorFlow release is also about data. Two of the main additions to the help make preprocessing smoother. I think preprocessing may very well be the most overlooked step and improving it is hugely beneficial.
- td.data.snapshot: allows you to run the preprocessing pipeline once, save the output and play with parameter optimization on top of that. Read more details in the RFC.
- Preprocessing layer API: package preprocessing logic inside a model for easier deployment.
To finish on a positive note, here is an awesome 3 minutes Lex Fridman’s video estimating costs for GPT to equal a human brain: