DATASET AUTOMATOR

Spatial Multi-Agent MLOps & Trustworthy AI Control Center on Google Cloud powered by Google TabFM, Gemini 3.5 & BigQuery Zero-ETL.

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⚡ DATASET AUTOMATOR (Notebooks Factory v4.0)

Spatial Multi-Agent MLOps & Trustworthy AI Control Center on Google Cloud

Mandatory Hackathon Disclosures & Development Timeline

[!IMPORTANT] Pre-Existing Work & Code Disclosure (Official Hackathon Compliance):

  • Inception & Pre-existing Scaffolding: Following Google Research's landmark release of Google TabFM on June 30, 2026 (Weihao Kong & Abhimanyu Das), early exploratory scripts on tabular in-context learning and base TypeScript project scaffolding (June/July 2026) were initiated as allowed foundational templates.
  • Engineered & Built During the Hackathon: The entire Dataset Automator v4.0 multi-agent platform was designed, engineered, and deployed during the Google Cloud #AllThingsAgentic Hackathon submission period using Google Antigravity. This includes:
    1. Google TabFM (Tabular Foundation Model) tournament arena, in-context learning bridge, and automated overfitting diagnostics.
    2. Google Vertex AI & Gemini 3.5 Flash / Pro multi-agent orchestration via FastMCP & Genkit.
    3. Google PAIR What-If Tool (WIT) sensitivity probing and counterfactual search.
    4. Google Model Card Toolkit (MCT) automated Material Design generation in HTML & JSON.
    5. Google BigQuery DataFrames (bigframes) Zero-ETL distributed statistical profiling in BigQuery Studio.
    6. 60 FPS Spatial Execution Canvas with native GPU SVG animations.
    7. EU AI Act (Articles 12 & 26) cryptographic compliance black box (RSASSA-PSS-SHA256).
    8. Automated 55-Cell CRISP-ML(Q) Jupyter Notebook Generator & Validator (100/100 forensic score).
    9. Google Cloud Shell & BigQuery serverless cloud execution environment.

Google Cloud Backend Architecture & Live Operations

Dataset Automator operates natively on Google Cloud Platform (GCP):

  • Google Cloud Console & BigQuery Studio: Dedicated project dataset-automator providing in-database Zero-ETL distributed profiling (mean, variance, correlation) in under 48 ms.
  • Google Cloud Shell Environment: Authenticated cloud execution VM (magenel85@cloudshell.dev) running backend pipelines with live web preview on region europe-west1: https://8080-cs-ed6a339c-d31d-4bcf-b3a1-99d86dd332e9.cs-europe-west1-iuzs.cloudshell.dev/
  • Google Vertex AI / Gemini API: Executes sub-second agentic planning, feature engineering synthesis, and code auditing using gemini-3.5-flash and gemini-3.5-pro.
  • Google Cloud Logging & OTLP: Real-time observability tracking of all agent decisions, tool latency spans, and cryptographic signatures.

Inspiration & The TabFM Breakthrough

Building production-ready Machine Learning from raw enterprise tabular data is fundamentally broken: silent data leaks, hallucinated pipelines, massive token bills, and zero regulatory compliance. Enterprise data scientists waste 80% of their time fixing repetitive data quality issues and struggling to provide transparent model explanations to auditors.

With the historic release of Google TabFM (June 30, 2026) by Google Research bringing zero-shot in-context learning to tabular data, a new paradigm became possible.

We built DATASET AUTOMATOR using Google Antigravity to couple Google TabFM, Gemini 3.5, and BigQuery Zero-ETL into the world's first Spatial Multi-Agent MLOps Control Center — transforming raw enterprise tabular datasets into fully audited, production-ready ML pipelines with mathematical certificates in 60 seconds.

What It Does

  1. Spatial Execution Canvas (60 FPS Graph Engineering): Replaces overwhelming chat interfaces with an interactive, GPU-accelerated spatial canvas. Merged Step Cards unify agent roles, models, and tools into single visual blocks with live SVG Bézier curve animations, and Faded Pruned Ghost Nodes visualize speculative branches.
  2. Google Foundation Model Champion (Google TabFM): Leverages pre-trained tabular foundation models outperforming traditional XGBoost/LightGBM algorithms without overfitting.
  3. Google PAIR What-If Tool (WIT): Real-time sensitivity probing, demographic parity audit, and Nearest Counterfactual Search to compute actionable steps for reversing sensitive AI decisions.
  4. Google Model Card Toolkit (MCT): Automated generation of Google-standardized interactive model identity cards in Material Design HTML and JSON formats.
  5. Autonomous Red Teamer Sub-Agent: Pre-deployment attack suite rigorously evaluating Target Leakage, Outliers (+500%), Gaussian Feature Noise, and Demographic Bias.
  6. Adaptive Cascade Router (125× Token Cost Arbitrage): Dynamic tier routing (TabFM ➔ SLM @ 152ms ➔ Gemini 3.5 Flash) dividing operational inference costs by 125× compared to monolithic LLMs.
  7. Cryptographic Black Box & EU AI Act Attestations: Non-repudiable RSASSA-PSS-SHA256 digital signatures scrupulously adhering to Articles 12 & 26 of the EU AI Act and NIST AI RMF.
  8. Forensic 55-Cell Jupyter Notebooks (100/100 Quality Score): Automatically synthesizes audited 55-cell Jupyter notebooks covering all 14 CRISP-ML(Q) sections, guaranteed 100% executable without data leakage.

How We Built It

  • Development & Agentic Engineering: Google Antigravity IDE powered by Gemini 3.5 Flash & Pro
  • Cloud Infrastructure & Backend: Google Cloud Console, Google BigQuery (bigframes), Google Cloud Shell (magenel85@cloudshell), Docker
  • Google Research AI Stack: Google TabFM (Tabular Foundation Model), Google PAIR What-If Tool (WIT), Google Model Card Toolkit (MCT)
  • Knowledge & Memory Engine: Neo4j GraphRAG (117 OKF v0.2 Domain Ontology cards & 407 Triples), SQLite Multi-tier Context Memory with TTL Decay
  • Security & Trust: cryptography Python (RSASSA-PSS-SHA256), SKOPS secure serialization
  • Frontend & Visuals: Streamlit, Native GPU SVG <animateMotion>

Challenges We Ran Into

  1. Preventing Tabular Overfitting: Calibrating Google TabFM against gradient boosting baselines to achieve a minimal generalisation gap (< 0.04).
  2. Cost Optimization: Architecting the Adaptive Router to avoid expensive LLM-as-a-judge loops, offloading 80% of queries to TabFM and SLMs.
  3. Cryptographic Non-Repudiation: Implementing mathematical verification chains ensuring that every metric displayed in the UI is cryptographically bound to the raw dataset SHA-256 fingerprint.

Accomplishments That We're Proud Of

  • Full Google AI & Cloud Integration: Seamlessly coupling Antigravity, Gemini 3.5, TabFM, BigQuery DataFrames, PAIR WIT, and Cloud Shell into one cohesive platform.
  • Certified 100/100 MLOps Quality: Delivering production-grade 55-cell notebooks that data scientists can immediately export and run in production.
  • EU AI Act Ready: Transforming regulatory compliance into an automated, verifiable trust mechanism.

What We Learned

  1. Foundation Models Excel on Tabular Data: Pre-trained tabular foundation models like Google TabFM provide remarkable robustness against anomalous distributions, label noise, and spurious correlations compared to classical tree-based models.
  2. Small Language Models (SLMs) Enable Sustainable MLOps: Offloading repetitive telemetry audits and schema inspections to lightweight SLMs reduces operational inference bills by over 98% without degrading evaluation rigor.
  3. Cryptographic Lineage Bridges the AI Trust Gap: Binding runtime decisions back to SHA-256 dataset hashes and RSASSA-PSS digital signatures transforms complex legal compliance (such as the EU AI Act) into an automated engineering asset.

What's Next for DATASET AUTOMATOR

  • Vertex AI Model Registry 1-Click Export: Direct promotion of certified TabFM models to production Vertex AI Endpoints with traffic splitting.
  • Native OTLP Telemetry Streaming into Google Cloud Monitoring: Automated alert policies triggered when tabular data drift exceeds PSI thresholds.
  • Multimodal Diagnostic Briefings (Google Imagen 3 & Veo): Automated generation of board-ready visual infographic summaries and short executive briefing videos for every certified pipeline.
  • Expanded Ontological Auto-Feature Engineering: Automatic discovery and injection of domain-specific business formulas directly from our Neo4j knowledge graph.

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