Tagged articles

7 articles

Page 1 of 1

AI Large-Model Wave and Transformation Guide

Apr 11, 2026 · Artificial Intelligence

How to Engineer Reliable AI Models: From Infrastructure to Deployment

This article presents a comprehensive, step‑by‑step framework for turning laboratory AI models into production‑ready systems, covering capability mapping, technology stack choices, model selection, prompt engineering, data pipelines, training strategies, and cross‑team collaboration to ensure stability, observability, and trustworthiness.

AI model engineeringModel DeploymentModel Monitoring

0 likes · 14 min read

How to Engineer Reliable AI Models: From Infrastructure to Deployment

DataFunTalk

Dec 28, 2022 · Artificial Intelligence

Automated Feature Engineering and Modeling for Credit Risk: A DataFun Case Study

This article explains how DataFun’s automated feature engineering and modeling platform dramatically reduces credit‑risk model development time from weeks to days by standardizing feature creation, integrating popular algorithms such as LR, XGBoost and LightGBM, and providing comprehensive evaluation, deployment and monitoring capabilities.

AIModel MonitoringRFM

0 likes · 14 min read

Automated Feature Engineering and Modeling for Credit Risk: A DataFun Case Study

AntTech

Dec 26, 2022 · Artificial Intelligence

AntSec MLOps: Building a Scalable, Automated, and Trustworthy AI Risk‑Control Platform

This article describes the challenges, overall architecture, data development, model monitoring, continuous training, security‑trustworthiness, and future roadmap of Ant Security's intelligent risk‑control platform, illustrating how AI, big data, and cloud computing are integrated to create a scalable, automated MLOps solution for dynamic fraud detection and mitigation.

AIMLOpsModel Monitoring

0 likes · 28 min read

AntSec MLOps: Building a Scalable, Automated, and Trustworthy AI Risk‑Control Platform

GuanYuan Data Tech Team

Dec 1, 2022 · Artificial Intelligence

Why MLOps Is the Key to Scalable AI Projects

This article explains the concept, significance, and practical case studies of MLOps—showing how integrating DevOps principles with data and machine learning creates reliable, automated pipelines for data quality, model monitoring, error analysis, and continuous integration, ultimately accelerating AI delivery.

AI EngineeringMLOpsMachine Learning Operations

0 likes · 15 min read

Why MLOps Is the Key to Scalable AI Projects

Code DAO

Dec 15, 2021 · Artificial Intelligence

Should You Monitor Your Machine Learning Models? An Introduction with Evidently AI

The article explains why monitoring production ML models is essential to detect data and target drift, describes the open‑source Evidently AI library and its statistical tests, and demonstrates its use on a weather‑forecast example and a plant‑seedling image classification case, including dashboards, code snippets, and visual analysis of drift impact.

Evidently AIModel MonitoringPython

0 likes · 14 min read

Should You Monitor Your Machine Learning Models? An Introduction with Evidently AI

360 Tech Engineering

Aug 22, 2018 · Artificial Intelligence

Rules of Machine Learning: 43 Practical Guidelines for Building Robust ML Systems

This article translates and summarizes Martin Zinkevich’s “Rules of ML”, offering 43 concise, experience‑based recommendations that cover terminology, pipeline design, feature engineering, monitoring, training‑serving consistency, and model iteration to help engineers build reliable machine‑learning‑driven products.

ML pipelineModel Monitoringbest practices

0 likes · 35 min read

Rules of Machine Learning: 43 Practical Guidelines for Building Robust ML Systems

AntTech

Apr 9, 2018 · Artificial Intelligence

Practical Guide to Modeling Stability: Feature PSI, Model PSI, and Monitoring Techniques

This article explains the importance of modeling stability, describes how to assess feature and model stability using the Population Stability Index (PSI), provides step‑by‑step calculation methods, and shares practical monitoring practices such as rank mapping and daily SQL‑based checks.

Model MonitoringModelingPSI

0 likes · 9 min read

Practical Guide to Modeling Stability: Feature PSI, Model PSI, and Monitoring Techniques