model monitoring

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.

AIMachine Learningautomated feature engineering

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.

AIautomationmlops

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 EngineeringMachine Learning Operationscontinuous integration

0 likes · 15 min read

Why MLOps Is the Key to Scalable AI Projects

GuanYuan Data Tech Team

Jun 16, 2022 · Artificial Intelligence

How Deepchecks Automates Data and Model Validation for Reliable AI Pipelines

This article introduces the open‑source Deepchecks library, explains its core concepts of checks, conditions, and suites, and provides step‑by‑step tutorials for data validation, train‑test validation, and model evaluation to help AI engineers build robust, data‑centric machine‑learning workflows.

data validationdeepchecksmlops

0 likes · 15 min read

How Deepchecks Automates Data and Model Validation for Reliable AI Pipelines

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 pipelineMachine Learningbest 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.

Data MiningMachine LearningPSI

0 likes · 9 min read

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