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Research#llm📝 BlogAnalyzed: Dec 28, 2025 12:02

Building a Machine Learning Infrastructure with BigQuery ML (BQML)

Published:Dec 28, 2025 11:23
1 min read
Qiita AI

Analysis

This article discusses the challenges of setting up a machine learning infrastructure, particularly the difficulty of moving data from a data warehouse (DWH) to a learning environment. It highlights BigQuery ML (BQML) as a solution, suggesting that it allows users to perform machine learning tasks using familiar SQL, eliminating the need for complex data pipelines and Python environment setup. The article likely goes on to explain the benefits and practical applications of BQML for simplifying the machine learning workflow. The core argument is that BQML lowers the barrier to entry for machine learning by leveraging existing SQL skills and infrastructure.
Reference

“DWHから学習環境へのデータ移動（パイプライン構築）”

PermalinkQiita AI
Machine Learning#BigQuery📝 BlogAnalyzed: Dec 28, 2025 11:02

CVR Prediction Model Implementation with BQ ML

Published:Dec 28, 2025 10:16
1 min read
Qiita AI

Analysis

This article presents a hypothetical case study on implementing a CVR (Conversion Rate) prediction model using BigQuery ML (BQML) and DNN models. It's important to note that the article explicitly states that all companies, products, and numerical data are fictional and do not represent any real-world entities or services. The purpose is to share technical knowledge about BQML and DNN models in a practical context. The value lies in understanding the methodology and potential applications of these technologies, rather than relying on the specific data presented.

Key Takeaways

Reference

“本記事は、BigQuery ML (BQML) および DNNモデルの技術的知見の共有を目的として構成された架空のケーススタディです。”

PermalinkQiita AI
Research#llm📝 BlogAnalyzed: Dec 25, 2025 18:10

[BQML] Completing Missing Values with Gemini Grounding (Google Search)

Published:Dec 25, 2025 09:20
1 min read
Zenn Gemini

Analysis

This article discusses using BigQuery ML (BQML) with Gemini and Grounding with Google Search to address the common problem of missing data in data analysis. Traditionally, filling in missing data required external scripts and APIs or manual web searches. The article highlights how this new approach allows users to complete this process using only SQL, streamlining the data completion workflow. This integration simplifies data preparation and makes it more accessible to users familiar with SQL. The article promises to detail how this integration works and its benefits for data analysis and utilization, particularly in scenarios where data is incomplete or requires external validation.
Reference

“データ分析や活用において、頻繁に課題となるのが 「データの欠損」 です。”

PermalinkZenn Gemini