ARTICLES

In this talk we’ll look at why contextual relevance in ad ranking and ad targeting is a must-have for a successful ad campaign; what are the current state-of-the-art machine learning solutions in the field of contextual advertising and ad ranking; and how...

By: Aparna Dhinakaran & Barr Moses As data and machine learning ecosystems become increasingly complex and companies ingest more and more data, it’s important that data and ML engineering teams go beyond monitoring to  understand the health of their data-driven...

Tell us a bit about yourself, your background, where you work, and what you do there. My journey is pretty varied and spans a couple of different areas, structured across a few different types of organizations, industry domains, and technologies. I’ve worked across...

In our last post we summarized the problem of drift in machine learning deployments (“Drift in Machine Learning: Why It’s Hard and What to Do About It” in Towards Data Science). One of the takeaways from the article is: methods for dealing with drift must identify...

Practical Green MLOps A manifesto on environmentally sustainable AI infrastructure When we consider the environmental impact of AI and MLOps, our attention is usually focused on the significant energy usage for the compute cycles required for training. After all, a...

tl;dr PipeRider is an open-source data reliability toolkit for identifying data quality issues across pipelines. PipeRider was created after months of industry research and it’s available now. Start learning more about the quality of your data by taking PipeRider for...

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...