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data preparation

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DataFunTalk
DataFunTalk
Jun 11, 2024 · Artificial Intelligence

Guide to Fine‑Tuning OpenAI Models for Improved Performance

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.

AI modelsAPIFine-tuning
0 likes · 16 min read
Guide to Fine‑Tuning OpenAI Models for Improved Performance
360 Quality & Efficiency
360 Quality & Efficiency
Aug 4, 2023 · Artificial Intelligence

Machine Learning Model Testing Workflow and Best Practices

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.

AIMachine LearningModel Deployment
0 likes · 7 min read
Machine Learning Model Testing Workflow and Best Practices
DataFunSummit
DataFunSummit
Jul 8, 2023 · Big Data

Data Preparation Practices at Douyin Group for Diverse Application Scenarios

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.

BICDPbig data
0 likes · 16 min read
Data Preparation Practices at Douyin Group for Diverse Application Scenarios
Data Thinking Notes
Data Thinking Notes
Dec 15, 2022 · Big Data

Why 80% of Data Analysis Time Is Spent on Data Preparation—and How to Master It

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.

ETLbig datadata governance
0 likes · 12 min read
Why 80% of Data Analysis Time Is Spent on Data Preparation—and How to Master It
DataFunSummit
DataFunSummit
Nov 10, 2021 · Artificial Intelligence

Applying Graph Neural Networks for Financial Risk Control: A Case Study by Shuhe Technology

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.

AIGNNGraph Neural Networks
0 likes · 12 min read
Applying Graph Neural Networks for Financial Risk Control: A Case Study by Shuhe Technology
Fulu Network R&D Team
Fulu Network R&D Team
Sep 14, 2020 · Product Management

Designing a Financial Reconciliation System: Objectives, Methods, Scenarios, and Process Steps

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.

data preparationdiscrepancy handlingfinancial system
0 likes · 10 min read
Designing a Financial Reconciliation System: Objectives, Methods, Scenarios, and Process Steps
DataFunTalk
DataFunTalk
Jun 8, 2020 · Artificial Intelligence

Augmented Analytics: Concepts, Key Technologies, and Practical Applications

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.

AutoMLMachine LearningNatural Language Processing
0 likes · 25 min read
Augmented Analytics: Concepts, Key Technologies, and Practical Applications
Architecture Digest
Architecture Digest
Mar 29, 2016 · Artificial Intelligence

Practical Guide to Machine Learning: Problem Modeling, Data Preparation, Feature Engineering, Model Training and Optimization

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.

Machine Learningdata preparationfeature engineering
0 likes · 17 min read
Practical Guide to Machine Learning: Problem Modeling, Data Preparation, Feature Engineering, Model Training and Optimization