ARTICLES

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...

8 Concept Drift Detection Methods

8 Concept Drift Detection Methods

There is a wide range of techniques that can be applied for detecting concept drift. Becoming familiar with these detection methods is key to using the right metric for each drift and model.  In the article below, I review four types of detection methods: Statistical,...

Guide to Data Labeling for Search Relevance Evaluation

Guide to Data Labeling for Search Relevance Evaluation

Photo by Markus Winkler on Unsplash Machine Learning (ML) has a number of applications in modern commerce, with Information Retrieval (IR) being one of the most common. Many e-businesses use it to gauge search quality relevance on their platforms to provide better...

How to Deal with Concept Drift in Production with MLOps Automation

How to Deal with Concept Drift in Production with MLOps Automation

Iguazio’s Data Scientist discusses how to detect and handle problems that arise when models lose their accuracy and how to implement concept drift detection and remediation in production. He shows how to automate MLOps processes at scale, to handle drift detection...

What Are Global, Cohort and Local Model Explainability?

What Are Global, Cohort and Local Model Explainability?

In the last decade, significant technological progress has been driven rapidly by numerous advances in applications of machine learning. Novel ML techniques have revolutionized industries by cracking historically elusive problems in computer vision, natural language...

An Introduction to Image Classification [+ Superb AI Tutorial]

An Introduction to Image Classification [+ Superb AI Tutorial]

Introduction In the world of computer vision, accuracy is critical. If your model isn’t detecting images correctly, then its application in the real world can be rendered useless, or worse, dangerous. A computer vision model that incorrectly identifies objects can...

Microsoft & GitHub on Git-Based CI / CD for Machine Learning & MLOps

Microsoft & GitHub on Git-Based CI / CD for Machine Learning & MLOps

David Aronchick, Head of OSS ML Strategy at Microsoft, Marvin Buss, Azure Customer Engineer at Microsoft, and Zander Matheson, Senior Data Scientist at Github discuss using Git to enable continuous delivery of machine learning to production, enable controlled...

Synthetic Time-Series Data: A GAN approach

Synthetic Time-Series Data: A GAN approach

Generate synthetic sequential data with TimeGAN Time-series or sequential data can be defined as any data that has time dependency. Cool, huh, but where can I find sequential data? Well, a bit everywhere, from credit card transactions, my everyday...

Building Great APIs for an AI-Powered Future

Building Great APIs for an AI-Powered Future

Modzy is an Artificial Intelligence (AI) platform designed to bridge the gap over what we call the “AI Valley of Death”, or the often difficult process of getting an AI or machine learning (ML) model out of the lab and into production in a secure and scalable way....

Using Statistical Distance Metrics for Machine Learning Observability

Using Statistical Distance Metrics for Machine Learning Observability

Statistical Distances are used to quantify the distance between two distributions and are extremely useful in ML observability. This blog post will go into statistical distance measures and how they are used to detect common machine learning model failure modes. Data...

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