Blog | super.AI

To create training data for ML projects, you need to work with human labelers. That means writing clear instructions on how to label your data. Here’s 12 rules for how to do this right.

The first costly surprise: machine learning lacks the clean abstraction of software. Leaky abstractions mean we can’t always rely on the contract that ML offers. This post examines what abstraction is, why it’s important, and how ML’s weak contracts can result in an ineffective system.

Everyone knows that machine learning (ML) has the power to create positive change, but not everyone is aware of its 7 biggest blockers you need to overcome to actually make it work in the real-world. This introductory post introduces the dark side of ML, ready to be combated in further instalments in the series.