Sublime

Since basically forever, the best designers have made custom tools to help them make their work. These often look like 'prototypes' or 'generators' or 'playgrounds', but are essential for quickly and repeatedly exploring and reproducing realistic results. This was often more easily done at... See more

ata Collection Experimentation Evaluation and Deployment Monitoring and Response Metadata Data catalogs, Amundsen, AWS Glue, Hive metas-tores Weights & Biases, MLFlow, train/test set parameter configs, A/B test tracking tools Dashboards, SQL, metric functions and window sizes Unit Data cleaning tools Tensorflow, ML-lib, PyTorch, Scikit-learn,... See more

Implementing ML requires high coordination, highly skilled engineering, and product development. Baseten's co-founders were data scientists who scrappily became full-stack engineers so they could use machine learning to mitigate fraud and abuse and moderate user-generated content. At Clover Health, Amir led teams that developed machine learning... See more

spotted on linkedin. need someone smart to remake this correctly, all of these should probably be above the water https://t.co/OUnMkbDlWb

Been playing around with Stable Diffusion. 1. I can finally let go of my meme/shitpost selection intern. 2. NFT grifters will no longer need to pay artists on Fiverr. https://t.co/Ui167ChAlg

How I visualized the so-very-overloaded AI engineer role (a la @swyx's https://t.co/2XbxmJ9Lbb) anyone aware of breakdown / articulation similar to this?

(1) Tasks (a) Data collection, cleaning & labeling: human annotators , exploratory data analysis (b) Embeddings & feature engineering: normalization , bucketing / binning , word2vec (c) Data modeling & experimentation: accuracy , F1-score , precision , recall (d) Testing: scenario testing , AB testing , adaptive test-data (2) Biz/Org Management (a)... See more