How To Organize Continuous Delivery of ML/AI Systems: a 10-Stage Maturity Model | Outerbounds
outerbounds.com
How To Organize Continuous Delivery of ML/AI Systems: a 10-Stage Maturity Model | Outerbounds
(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 Ma... See more
Shreya Shankar • "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning.
Software development is an iterative design process. In order to achieve true iteration and realize the value of fast feedback, its activities need to be performed with a single team (having a common reporting line) as far as possible. Many internet business and independent software vendors (ISVs) already operate in this way.
Sriram Narayan • bliki: ActivityOriented
Technology Infrastructure + Artificial Intelligence Is there a way to structure, pipe and manage data better?Are there ways to improve productivity given the information overload and noise we have in our work communication?Can we make access to information affordable?Can we assist in critical machine to machine communication?Can we make it easy to ... See more