ARTICLES

Understanding ML Monitoring Debt

Understanding ML Monitoring Debt

We’re all familiar with technical debt in software engineering, and at this point, hidden technical debt in ML systems is practically dogma. But what is ML monitoring debt? ML monitoring debt is when model monitoring is overwhelmed by the scale of the ML systems that...

5 Minimum Requirements of an Operational Feature Store

5 Minimum Requirements of an Operational Feature Store

I’ve spent the last few months thinking heavily about feature stores. It’s the hottest new buzz word in the ML space, and everyone has a distinct implementation laser-focused on their personal use cases. A recent article¹ that I read talked about this exact topic and...

Lead with DevSecOps to Lower Risk and Raise Value

Lead with DevSecOps to Lower Risk and Raise Value

Developing and deploying AI-powered systems and applications is a complex business, especially in our extended remote reality. You’re likely facing an uphill climb and let’s face it, huge risks. The way to clear the obstacles, lower the risks, and raise the value you...

What are AI attacks?

What are AI attacks?

What are AI attacks? Introduction AI is the gamechanger of this decade. It is rapidly transforming our world and everyday life. The underlying technology, called Machine Learning (ML), is all around us. It’s ML that decides whether you get a loan sanctioned, how much...

Model Monitoring for Financial Fraud Classification

Model Monitoring for Financial Fraud Classification

Every $1 of fraud loss costs financial services firms $4 in losses [1]. These losses stem from incurred interest, fines and legal fees, labor and investigation costs, and external recovery expenses. To avoid this, financial services firms deploy machine learning...

Data Exchange Podcast (Episode 79) Hyun Kim

Data Exchange Podcast (Episode 79) Hyun Kim

This week’s guest is Hyun Kim, co-founder and CEO of Superb AI, a startup building tools to help companies manage data across the entire machine learning application lifecycle. This includes tools to label, store, and monitor data assets that power all computer vision...

Guest Post: “ML Data”: The Past, Present, and Future

Guest Post: “ML Data”: The Past, Present, and Future

In this article, co-founder and CTO of Galileo Atindriyo Sanyal gives a fascinating overview of the ‘ML data intelligence’ evolution and shares a few insights on why the organizations that obsess on their ML data quality will quickly greatly outperform those that...

High-quality data meets enterprise MLOps

High-quality data meets enterprise MLOps

According to the 2021 enterprise trends in machine learning report by Algorithmia, 83% of all organizations have increased their AI/ML budgets year-on-year, and the average number of data scientists employed has grown by 76% over the same period. However, the process...

Resilient human-in-the-loop pipelines with Pachyderm and Toloka

Resilient human-in-the-loop pipelines with Pachyderm and Toloka

Why data prep is hard Many data scientists and machine learning teams report that they spend about 80% of their time preparing, managing, or curating their datasets. There are three things that have enabled the ML revival over the last 5–10 years: breakthroughs in...

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