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product#agent📝 BlogAnalyzed: Jan 4, 2026 09:24

Building AI Agents with Agent Skills and MCP (ADK): A Deep Dive

Published:Jan 4, 2026 09:12
1 min read
Qiita AI

Analysis

This article likely details a practical implementation of Google's ADK and MCP for building AI agents capable of autonomous data analysis. The focus on BigQuery and marketing knowledge suggests a business-oriented application, potentially showcasing a novel approach to knowledge management within AI agents. Further analysis would require understanding the specific implementation details and performance metrics.
Reference

“はじめに”

PermalinkQiita AI
Research#AI Analysis Assistant📝 BlogAnalyzed: Jan 3, 2026 06:04

Prototype AI Analysis Assistant for Data Extraction and Visualization

Published:Jan 2, 2026 07:52
1 min read
Zenn AI

Analysis

This article describes the development of a prototype AI assistant for data analysis. The assistant takes natural language instructions, extracts data, and visualizes it. The project utilizes the theLook eCommerce public dataset on BigQuery, Streamlit for the interface, Cube's GraphQL API for data extraction, and Vega-Lite for visualization. The code is available on GitHub.
Reference

“The assistant takes natural language instructions, extracts data, and visualizes it.”

PermalinkZenn AI
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
Research#llm📝 BlogAnalyzed: Dec 25, 2025 05:55

Cost Warning from BQ Police! Before Using 'Natural Language Queries' with BigQuery Remote MCP Server

Published:Dec 25, 2025 02:30
1 min read
Zenn Gemini

Analysis

This article serves as a cautionary tale regarding the potential cost implications of using natural language queries with BigQuery's remote MCP server. It highlights the risk of unintentionally triggering large-scale scans, leading to a surge in BigQuery usage fees. The author emphasizes that the cost extends beyond BigQuery, as increased interactions with the LLM also contribute to higher expenses. The article advocates for proactive measures to mitigate these financial risks before they escalate. It's a practical guide for developers and data professionals looking to leverage natural language processing with BigQuery while remaining mindful of cost optimization.
Reference

“LLM から BigQuery を「自然言語で気軽に叩ける」ようになると、意図せず大量スキャンが発生し、BigQuery 利用料が膨れ上がるリスクがあります。”

PermalinkZenn Gemini
Technology#Artificial Intelligence📝 BlogAnalyzed: Jan 3, 2026 07:21

Google Announces Full-Managed MCP Server for AI Integration Across Services

Published:Dec 10, 2025 23:56
1 min read
Publickey

Analysis

Google is expanding its AI integration capabilities by offering a fully managed MCP server that connects its generative AI models (like Gemini) with its cloud services. This unified layer simplifies access and management across various Google and Google Cloud services, starting with Google Maps, BigQuery, and Google Compute Engine. The announcement suggests a strategic move to enhance the accessibility and usability of AI within its ecosystem.
Reference

“Google's existing API infrastructure is now enhanced to support MCP, providing a unified layer across all Google and Google Cloud services.”

PermalinkPublickey
Technology#AI, Data Analysis, NLP👥 CommunityAnalyzed: Jan 3, 2026 09:45

Hacker News Activity Analysis with GPT-4 Agent

Published:Dec 20, 2023 14:42
1 min read
Hacker News

Analysis

The article describes the use of a data bot, Dot, to analyze Hacker News data using GPT-4 and BigQuery. It focuses on demonstrating the bot's capabilities by analyzing HN data and visualizing it with Plotly. The authors invite user feedback for further analysis.
Reference

“We thought we'd demo it using the tried and true method of "show Hacker News stuff about itself".”

PermalinkHacker News
Research#llm📝 BlogAnalyzed: Dec 29, 2025 08:22

Applied Machine Learning for Publishers with Naveed Ahmad - TWiML Talk #182

Published:Sep 20, 2018 20:56
1 min read
Practical AI

Analysis

This article summarizes a podcast episode featuring Naveed Ahmad, Senior Director of data engineering and machine learning at Hearst Newspapers. The discussion centers on Hearst's implementation of machine learning, exploring their motivations, early projects, data acquisition challenges within a large organization, and the advantages of using Google's BigQuery. The episode provides insights into the practical application of ML in the publishing industry, highlighting both opportunities and hurdles.
Reference

“In our conversation, we discuss into the role of ML at Hearst, including their motivations for implementing it and some of their early projects, the challenges of data acquisition within a large organization, and the benefits they enjoy from using Google’s BigQuery as their data warehouse.”

PermalinkPractical AI
Technology#Machine Learning, BigQuery, Cloud Computing👥 CommunityAnalyzed: Jan 3, 2026 15:43

Machine Learning in Google Bigquery

Published:Jul 25, 2018 17:32
1 min read
Hacker News

Analysis

The article's title suggests a focus on the application of machine learning within Google BigQuery. This implies an exploration of how users can leverage BigQuery's capabilities for machine learning tasks, potentially including model training, prediction, and data analysis. The source, Hacker News, indicates a technical audience interested in data engineering, cloud computing, and machine learning.
Reference

“”

PermalinkHacker News