Model Drift

Model drift refers to the phenomenon where the performance of a machine learning model degrades over time after its deployment. This occurs when the underlying data distribution changes, leading to discrepancies between the model’s training data and the real-world data it encounters. Factors contributing to model drift include external events, shifts in user behavior, and changes in the context in which the model operates. Understanding and monitoring model drift is crucial for maintaining the accuracy and reliability of machine learning systems, as it can significantly impact decision-making processes and outcomes in various applications.

Model Drift Introduction and Concepts

Towards Data Science

Taxes, death and model drift are the only three certainties in life. Ok, I might have added this last one to the adage but the truth is that all models suffer from decay.

Machine Learning Model Drift

Towards Data Science

Types, causes, detections, mitigations, and tools Continue reading on Towards Data Science

📊 Edge#37: What is Model Drift?

TheSequence

In this issue: we explain what model drift is; we overview the pillars of robust machine learning summarized by DeepMind; we discuss Fiddler, an ML monitoring platform with built-in model drift detect...

Unboxing the Concept of Drift in Machine Learning

Towards AI

Machine Learning Drift is a common phenomenon that occurs once the machine learning algorithm is deployed to production. It can adversely affect the overall performance of your machine-learning model ...

The Ultimate Guide to Understanding Model Drift in Machine Learning

Towards AI

Deploying machine learning models into production requires every data scientist to be prepared for what's ahead. This article will help you understand model drift in-depth.

Concept Drift and Model Decay in Machine Learning

Towards Data Science

Concept drift is a drift of labels with time for the essentially the same data. It leads to the divergence of decision boundary for new data from that of a model built from earlier data/labels…

Drift in Machine Learning

Towards Data Science

The COVID-19 pandemic has sparked a lot of interest in data drift in machine learning. Drift is a key issue because machine learning often relies on a key assumption: the past == the future. In the…

Getting a Grip on Data and Model Drift with Azure Machine Learning

Towards Data Science

Detect, analyze, and mitigate data and model drift in an automated fashion By Natasha Savic and Andreas Kopp Change is the only constant in life. In machine learning, it shows up as drift of data, mo...

How to Detect Data Drift with Hypothesis Testing

Towards Data Science

Data drift is a concern to anyone with a machine learning model serving live predictions. The world changes, and as the consumers’ tastes or demographics shift, the model starts receiving feature…

How to Detect Drift in Machine Learning Models

Towards Data Science

Have you ever gotten awesome results on your test set only to have your models perform poorly in production after some time? If so, you might be experiencing model decay. Model decay is the gradual…

“My data drifted. What’s next?” How to handle ML model drift in production.

Towards Data Science

This data drift might be the only signal. You are predicting something, but don’t know the facts yet. Statistical change in model inputs and outputs is the proxy. The data has shifted, and you…

Tidying up the framework of dataset shifts

Towards Data Science

Taking a step back about the causes of model degradation In collaboration with Marco Dalla Vecchia as the image creator We train models and use them to predict certain outcomes given a set of inputs....