AI Reveals Explosive Growth of Floating Algae Across the World’s Oceans

Machine learning reveals that ocean conditions are increasingly becoming more favorable for macroalgae growth.
Chiang Rai Times

Claude Opus 4.7 Release: Features, Benchmarks, and Access After Launch

Anthropic dropped Claude Opus 4.7 on April 16, 2026, just days ago. A leak had the AI community buzzing for weeks beforehand. Now it's here, and it's their ...
The American Journal of Managed Care

Machine Learning Model Improves Prediction of Heart Failure Risk in CKD

A machine learning model using routine clinical data more accurately predicted 5-year heart failure risk in patients with CKD than traditional tools. Heart failure is one of the most serious and ...
marktechpost

Safely Deploying ML Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing)

Deploying a new machine learning model to production is one of the most critical stages of the ML lifecycle. Even if a model performs well on validation and test datasets, directly replacing the ...
IEEE

Self-Adaptive Framework for Deploying Machine Learning Systems without Ground-Truth Data during Operation

Abstract: In recent years, the practical application of machine learning technology has rapidly progressed, accelerating its adoption across various fields. In this context, studies into the effective ...
Digital Trends

You can now use Gemini’s Thinking model without worrying about Pro model limits

Google has quietly reworked Gemini‘s usage limits, splitting the shared pool and boosting the individual caps for the Thinking and Pro models. At launch, both models had the same daily quota, meaning ...
The American Journal of Managed Care

Machine Learning Predicts Hyperglycemia Risk in Psoriasis

The XGBoost model predicts hyperglycemia risk in psoriasis patients with high accuracy, achieving an AUC of 0.821 in the training set. A web-based calculator was developed to facilitate personalized ...
marktechpost

An Implementation on Building Advanced Multi-Endpoint Machine Learning APIs with LitServe: Batching, Streaming, Caching, and Local Inference

In this tutorial, we explore LitServe, a lightweight and powerful serving framework that allows us to deploy machine learning models as APIs with minimal effort. We build and test multiple endpoints ...
GitHub

Dockerize and deploy machine learning model as REST API using Flask

A simple Flask application that can serve predictions machine learning model. Reads a pickled sklearn model into memory when the Flask app is started and returns predictions through the /predict ...