The article introduces time‑series data, its ubiquitous examples, the challenges of traditional small models, and proposes a universal time‑series large model that simplifies data preparation and model building, ultimately enabling more efficient and stable industrial AI solutions, now offered as a cloud service.
The article reveals five hard‑won truths for enterprises adopting large AI models, showing why buying GPUs first often stalls projects and outlining how to define business goals, start with API‑based pilots, run small‑scale trials, invest in data pipelines, and build robust evaluation frameworks.
This article provides a comprehensive guide to customizing pre‑trained large language models through fine‑tuning techniques—including parameter‑efficient methods, data preparation, knowledge injection, and robust evaluation—offering practical steps, best practices, and domain‑specific considerations for achieving superior task performance.
The article breaks down multiple practical approaches for data preparation, training code handling, and experiment analysis in large‑language‑model fine‑tuning, showing how deeper engagement in each step can boost personal expertise even when final model performance appears similar.
This guide explains how to fine‑tune OpenAI’s pre‑trained models, covering data preparation, environment setup, API usage, code examples, hyper‑parameter tuning, monitoring, and best practices to achieve better performance with less data and compute resources.
This guide reviews six popular Snowflake‑compatible tools—covering data preparation, visualization, integration/ETL, business intelligence, and governance—that can dramatically boost productivity for data professionals.
This article outlines the essential concepts, data preparation, model creation, training, deployment, and verification steps for testing machine‑learning models, highlighting dataset requirements, algorithm categories, framework choices, resource considerations, and provides a sample inference request.
This article explains Douyin Group's large‑scale data applications, introduces the concept and architecture of data preparation, details its four subsystems and modular capabilities, and showcases how these are applied in BI, CDP, and custom scenarios within the Volcano Engine ecosystem.
Data preparation consumes about 80% of the entire analytics workflow, making data collection, quality assurance, and governance critical pillars—spanning metadata, master data, storage layers like data lakes and warehouses, and rigorous preprocessing—to turn raw information into reliable insights.
This article explains how the open‑source Structured Process Language (SPL) streamlines report data preparation, replaces complex SQL and Java code, supports multi‑source processing, enables hot‑swap, and dramatically reduces development effort and performance bottlenecks.
This article describes how Shuhe Technology leveraged graph neural networks to improve financial risk assessment by preparing massive relational graph data, selecting DGL as the development framework, designing a GraphSage‑GAT model, addressing data sparsity and imbalance, and achieving notable AUC gains over traditional methods.
This article outlines the purpose, methods, common scenarios, user groups, and a five‑step workflow—including data preparation, cleaning, verification, discrepancy handling, and result reporting—for designing an effective financial reconciliation system.
This guide outlines why manual testing can’t fully replace automation, then walks you through seven concrete steps—including data preparation, API testing, and web UI automation using tools like MySQL, Redis, SOAP UI, Postman, and Selenium with Java or Python—to become a proficient automation test engineer.
This article explains the concept of augmented analytics, compares it with traditional BI, outlines its impact on data preparation, analysis, and machine learning, and reviews the underlying technologies such as NLQ, NLG, AutoML, and data robots, supported by Gartner insights and industry examples.
This article presents a comprehensive, practical guide to applying machine learning in industry, covering problem modeling, data preparation, feature extraction, model training, and optimization, illustrated with a DEAL transaction amount forecasting case study.
This article outlines a complete industrial machine‑learning workflow—starting with problem modeling, through data preparation, feature extraction, model training, and ending with model optimization—illustrated with a real‑world DEAL revenue‑prediction case and practical tips for handling data, features, and model selection.
This article outlines how Meituan applies machine learning to industrial challenges by detailing the full workflow—from problem modeling and data preparation to feature engineering, model training with algorithms like Logistic Regression and GBDT, and optimization techniques that address underfitting and overfitting for large‑scale deployment.
