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Google Cloud's AI platform has gone through several names over the years — Cloud ML Engine, then AI Platform, then Vertex AI, then Gemini Enterprise Agent Platform, and unified under the Gemini Enterprise umbrella (which brings together the developer Agent Platform, the employee-facing Gemini Enterprise app formerly known as Agentspace, and Gemini Enterprise for Customer Experience). Each name change tracked a real expansion in scope: from custom model training, to a unified ML platform with AutoML and managed notebooks, to the current platform where model building, agent development, evaluation, and enterprise governance live side by side.
This repository (statmike/gcp-mlops, formerly vertex-ai-mlops) has grown along that same path. It started as a collection of end-to-end training and serving workflows on Vertex AI and has expanded across the Google Cloud data and AI stack:
- In-database ML, GenAI, and causal inference in BigQuery (
CREATE MODEL,ML.*, andAI.*functions) — now also packaged as the standalone repositorystatmike/bigquery-data-scienceand on PyPI (bigquery-data-science) - AI agents and multi-agent architectures built with ADK, MCP, MCP Toolbox for Databases, and A2A, deployed on Agent Engine
- Cross-service MLOps infrastructure — online and batch serving, five feature store architectures, Vertex AI Pipelines (KFP), Managed Airflow (formerly Cloud Composer), Dataflow (
RunInference), and Dataproc Serverless Spark - Database-backed AI across Spanner, AlloyDB, Cloud SQL, Bigtable, Memorystore/Valkey, and Firestore for feature serving, vector search, and SQL-based inference
- Packaged Agent Skills, CLI & Stdio MCP Server (
SKILL.mdwith 4-Lens progressive disclosure) distilled from the notebooks and reference guides so AI coding agents can use the verified patterns and gotchas directly (uvx bigquery-data-science installornpx skills add statmike/bigquery-data-science)
Across this repository and its companion statmike/bigquery-data-science repository you will find 580+ interactive notebooks, 85+ standalone SQL scripts, and deep reference guides. Every workflow is written to be a clear, runnable starting point — heavy compute happens on Google Cloud services rather than your local machine, and related notebooks share datasets so you can compare approaches head to head.
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Start["statmike/gcp-mlops<br/>580+ Notebooks · 85+ SQL Scripts · Packaged Agent Skills"]
P1["1. Data + AI & In-Database ML (data+ai/ + statmike/bigquery-data-science)<br/>BigQuery ML (CREATE MODEL · ML.* Functions · Causal & MLOps Pipelines)<br/>BigQuery AI (AI.* & Remote ML.* Functions · RAG & Hybrid Search) · Dataflow · Dataproc · Iceberg"]
P2["2. Agent Skills, CLI & MCP (agent-skills/ → PyPI: bigquery-data-science)<br/>3 4-Lens Skills: bigquery-data-science · bigquery-ml · bigquery-ai"]
P3["3. Applied ML, AI Agents & Solutions (Applied ML/ — 29 notebooks · 60+ guides)<br/>9 AI Agent Projects (Gemini Enterprise Tour · 1,800-Cell NL2SQL MCP Benchmark)<br/>3 Solution Prototypes (Doc Fraud · 9-Way Taxonomy · Forecasting) · Vizier Optimization"]
P4["4. MLOps Infrastructure (MLOps/ — 100 notebooks)<br/>5 Feature Stores (BigQuery · Spanner · Bigtable · Valkey · Vertex AI)<br/>6 Model Serving Patterns (Online · Batch · SQL · Containers · Triton · vLLM) · KFP"]
P5["5. Applied GenAI, Frameworks & Forecasting (117 notebooks)<br/>11-Database Vector Retrieval Comparison (Applied GenAI/ — 58 notebooks)<br/>Framework Workflows (PyTorch · R · AutoML · Keras · CatBoost · Flax) · Forecasting (20)"]
Start --> P1 --> P2 --> P3 --> P4 --> P5
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| If you want to... | Go here |
|---|---|
| Train, evaluate, explain, or schedule models directly in BigQuery SQL | data+ai/bq-ml/ · statmike/bigquery-data-science/bigquery-ml |
| Call Gemini, generate embeddings, run hybrid search, or zero-shot forecast in SQL | data+ai/bq-ai-functions/ · statmike/bigquery-data-science/bigquery-ai |
| Give your AI coding agent (Claude Code, Antigravity, Gemini CLI, Codex) verified BigQuery skills, CLI & MCP | agent-skills/ · statmike/bigquery-data-science · PyPI |
| Build, govern, benchmark, and deploy agents with ADK, MCP, A2A, and Agent Engine | Applied ML/AI Agents/ |
| Compare 5 feature store architectures (BigQuery, Spanner, Bigtable, Valkey, Vertex AI) | MLOps/Feature Store/ |
| Compare serving patterns (Online, Batch, SQL, Cloud Run, GKE, Triton, vLLM) | MLOps/Serving/ |
| Compare 11 Google Cloud databases for vector search and RAG retrieval | Applied GenAI/Retrieval/ |
| Compare ML training & batch inference across 7 GCP compute services on the same dataset | data+ai/overview/ml-training/ |
| See end-to-end multi-service applications (Document Fraud, 9-Way Taxonomy, Forecasting) | Applied ML/Solution Prototypes/ |
| Train and serve with PyTorch, Keras, CatBoost, Flax, AutoML, or R across GCP runtimes | Framework Workflows/ |
Each section below has its own detailed readme.md with vertical architecture diagrams and full notebook/SQL inventories:
Machine learning, generative AI, causal inference, and data engineering across Google Cloud's data services — bringing model training, foundation-model inference, and orchestration directly to where the data lives. The BigQuery ML and BigQuery AI curricula live in their dedicated repository (statmike/bigquery-data-science) and Python package (bigquery-data-science on PyPI) with local signpost hubs here in data+ai/:
-
BigQuery ML (
bq-ml/) (GitHub Hub · Overview · Reference) — Per-model lifecycle deep dives (CREATE MODEL), model-freeML.*function suites, end-to-end SQL workflows (including Causal Inference & Econometrics: Propensity Score Matching/IPTW, Double ML Price Elasticity, Uplift/CATE, Difference-in-Differences with Callaway–Sant'Anna, Synthetic Control,AI.CAUSAL_EFFECT, and Survival Analysis, plus Point-in-Time Feature Store leakage quantification and Hierarchical Forecasting), and production orchestration pipelines (SQL Scripting, Scheduled Queries, Dataform, Cloud Workflows, dbt, Vertex KFP, and Managed Airflow). -
BigQuery AI (
bq-ai-functions/) (GitHub Hub · Overview · Reference) — Function-by-function guides (AI.GENERATE*,AI.CLASSIFY,AI.IF,AI.SCORE,AI.AGG,AI.EMBED,VECTOR_SEARCH,AI.SEARCH,AI.FORECAST,AI.DETECT_ANOMALIES,AI.PREDICTTabFM,AI.CAUSAL_EFFECT,AI.KEY_DRIVERS, and pre-trained Cloud AI services) plus composed multi-function workflows (RAG, Hybrid Catalog Search with Rank Fusion, Web-Grounded Enrichment, Document & Time Series Intelligence, and Image Deduplication). -
Cross-Service Data + AI — 7-Way ML Training & Batch Inference Comparison, Dataflow
RunInference& GPU Benchmark, Dataproc Serverless Spark, Managed Airflow, BigQuery Managed Iceberg, BigQuery Continuous Queries, Tabular Data Read Benchmarks, and SQL inference in AlloyDB & Spanner. -
$\rightarrow$ Full catalog & Data + AI decision flow:data+ai/readme.md
2. Agent Skills, CLI & Stdio MCP (agent-skills/) — bigquery-data-science on GitHub & PyPI
Packaged, cross-tool Agent Skills distilled from live-tested BigQuery ML and BigQuery AI notebooks into 4 progressive lenses (SKILL.md routers reference/*.md decision guides <25 KB syntax/*.md cards narrative/*.md textbook walkthroughs), shipped as a zero-dependency Python package (bigquery-data-science on PyPI) and standalone repository (statmike/bigquery-data-science):
-
bigquery-data-science(umbrella triage router +ML.*vsAI.*namespace matrix) ·bigquery-ml·bigquery-ai. - Install into any repository or global agent config with
uvx bigquery-data-science installornpx skills add statmike/bigquery-data-science, query from the terminal (uvx bigquery-data-science triage | lookup | search), or attach as a stdio MCP server (uvx bigquery-data-science mcp). -
$\rightarrow$ Full guide & installation options:agent-skills/README.md·statmike/bigquery-data-science· PyPI
3. Applied ML, AI Agents & Solution Prototypes (Applied ML/) — 29 notebooks & 60+ guides
Where MLOps/ focuses on serving and infrastructure, Applied ML/ focuses on what you build with models — autonomous multi-agent systems, multi-service business applications, and black-box optimization:
-
AI Agents (
Applied ML/AI Agents/) (9 projects built with ADK, MCP, MCP Toolbox for Databases, and A2A) — Led by Agent Building: A Tour of the Gemini Enterprise Agent Platform (exercising Agent Runtime, Memory Bank, Identity, Registry, Model Armor, and a mixed Gemini 3 + Claude fleet) and the Data MCP Sandbox (a controlled 1,800-cell / 12-arm empirical NL2SQL benchmark measuring the exact accuracy and token-cost impact of Dataplex/Looker governance and MCP tool schemas), plus BigQuery Context Discovery, Data Onboarding, RFI Response Agent, Image-to-Graph Agent, Concept-BQ, Conversational Analytics API, and Travel Planner (A2A). -
Solution Prototypes (
Applied ML/Solution Prototypes/) (13 notebooks) — Document Processing & Fraud Detection (8 notebooks), Product Hierarchy Classification (9 approaches across flat, top-down cascade, and Keras Multi-Task Learning, both Python-first and SQL-first), and Conversational Time Series Forecasting (3 notebooks). - Optimization with Vertex AI Vizier, Hierarchical Forecasting, and Anomaly Detection.
-
$\rightarrow$ Full catalog & Applied ML architecture map:Applied ML/readme.md
4. MLOps Infrastructure (MLOps/) — 100 notebooks
Everything between training a model and operating a fleet of models in production:
-
Feature Stores (
MLOps/Feature Store/) (45 notebooks + 5 Architecture Slide Decks) — Five architectures built on the same BigQuery offline data across the latency/cost spectrum: BigQuery (3), Spanner (12), Bigtable (15), Valkey / Memorystore (9), and Vertex AI Feature Store (6). -
Model Serving (
MLOps/Serving/) (32 notebooks) — Online Endpoints (7), Batch Inference (4), SQL Inference (5), Cloud Run / GKE / Cloud Functions (4), NVIDIA Triton (5), and vLLM (6). - Vertex AI Pipelines / KFP (13 notebooks), Model Evaluation (3), Model Monitoring (2), Experiment Tracking (1), and Development Environments (2).
-
$\rightarrow$ Full catalog & MLOps stack diagram:MLOps/readme.md
5. Applied GenAI (Applied GenAI/) — 58 active notebooks
Grounding generative models with your own data across the full RAG and multimodal pipeline:
-
Vector Search & Retrieval Across 11 Databases (
Retrieval/) (14 notebooks comparing BigQuery, Vertex Vector Search, Feature Store, Spanner, AlloyDB, Cloud SQL, Memorystore/Valkey, Firestore, Bigtable, Local, and ChromaDB), Generate (13), Embeddings (9), Solutions & Hybrid Search (Dense + BM25 + RRF) (7), RAG Engine (1), Structure-Aware Chunking (3), Evaluation (2), Ranking (1), and Validate (1). -
$\rightarrow$ Full catalog & GenAI pipeline diagram:Applied GenAI/readme.md
6. Framework Workflows, Applied Forecasting & Foundational Series
- Framework Workflows (
Framework Workflows/) (39 notebooks) — Shared BigQuery dataset, Feature Store, and Model Registry exercised across PyTorch (20 notebooks across 19 serving targets), R on Google Cloud (6 notebooks across 4 runtimes + Iceberg/arrow), Vertex AI AutoML (6), Keras 3 (3), CatBoost (3), and Flax / JAX (1). - Applied Forecasting (
Applied Forecasting/) (20 notebooks) — Progressive time-series curriculum on NYC Citi Bike data:ARIMA_PLUS,ARIMA_PLUS_XREG, regression lags, AutoML Forecasting, Seq2Seq+, TFT, TiDE, Prophet (training, containers, online CPR), TimesFM & TimesFM 2.5, and KFP forecasting tournaments. - Foundational Numbered Series & Utilities:
00 - Setup·01 - Data Sources·02 - Vertex AI AutoML(6) ·03 - BigQuery ML (BQML)(26) ·04 - scikit-learn(12) ·05 - TensorFlow(28) ·Working With/Document AI(8) ·Tips(13) ·architectures/(28) ·readme-legacy.md(complete historical index).
Every notebook in this repository is designed around three practical rules:
- Self-contained and portable — Narrative explanations, architecture diagrams, setup cells, and cleanup cells live in a single
.ipynbfile (with one-click links in the header to open in Colab, Colab Enterprise, BigQuery Studio, or Vertex AI Workbench). - Cloud-orchestrated compute — Notebooks orchestrate BigQuery, Vertex AI, Dataflow, Dataproc, and Cloud Run rather than crunching large datasets locally, so almost every notebook runs comfortably on the smallest standard runtime.
- Application Default Credentials (ADC) only — No service account JSON keys are ever created or stored; local workflows use
gcloud auth application-default loginand cloud runtimes use attached service accounts.
- Contributing: Issues, discussions, and pull requests are welcome — see CONTRIBUTING.md, CODE_OF_CONDUCT.md, and SECURITY.md.
- License: Apache 2.0 — see LICENSE.
This repository is maintained by Mike Henderson (@statmike). It is built on Google Cloud products for learning and reference and is not an officially supported Google product.
