Updates and perspectives from our team

Santa reached out to Canotic to help him build some machine learning models to make his work a little easier. Here's how we helped.

95% of the code in an ML model is actually just “plumbing”: code that handles configuration, feature extraction, monitoring, analysis, resource management, serving production models, etc.. In this post we talk about glue code, processing spaghetti code and how to tackle dead experimental code paths.

AI is transforming agriculture. We explore use cases showing how machine learning is applied in the industry to increase yields and resourcefulness.

In ML systems, data dependencies carry a lot of depth but are more difficult to predict. In this article, we explore the importance of quality input data and introduce a new danger: feedback loops.

The second costly surprise: machine learning often evolves into a complex web of interlinking systems. Changing anything in the system can have wide-reaching and potentially negative effects far from the source of the change. This post explores how to avoid the problems such as a system poses.

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.

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.

Real-world applications of ML require extremely high accuracy to provide a consumer benefit. Increasing accuracy is a balancing act between bias and variance. To minimize both, and thereby increase accuracy, ML models requires plentiful high quality training data.

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.