Introduction: AI is a rapidly evolving field and up until a few years ago there were distinct model architectures for different tasks, for example, CNN was the go-to for vision tasks and LSTM like networks for language tasks. Then, with the invention of the...

This blog series focuses on unfairness that can be obscured when looking at data or the behavior of an AI model according to a single attribute at a time, such as race or gender. We first describe a real-world example of bias in AI and then discuss fairness,...

An immutable database means the data within it cannot be deleted or modified. There are numerous reasons why an immutable database is beneficial for you and this article explains some of those arguments. Martin Kleppmann, who is a serial entrepreneur and...

In this session Yaron Haviv discusses how to enable continuous delivery of machine learning to production using Git based ML pipelines (Github Actions) with hosted training and model serving environments. Yaron touches upon how to leverage Git to solve rigorous MLOps...

In this article, we’ll talk about different roles in a data team and discuss their responsibilities. In particular, we will cover: The types of roles in a data team; The responsibilities of each role; The skills and knowledge each role needs to have. Want to listen to...

Avoid these common ML monitoring mistakes – your model’s success hangs in the balance.  So you’ve built a machine learning model that works well in the lab. You’ve validated it, gotten the green light from the internal stakeholders, ensured that it met any regulatory...