This is not an official Google product.
A collection of Jupyter notebooks exploring Google Cloud AI, ML, and developer tools.
| Notebook | Description |
|---|---|
| 🕵️ Gemini Deep Research Agent | Autonomously plan, execute, and synthesize multi-step research tasks using the Gemini Deep Research agent and the Interactions API. |
| 🚀 Cloud Run LLM Serving with Cloud Storage FUSE | Deploy large language models on Cloud Run, using Cloud Storage FUSE for efficient model management. |
| 📦 Google Drive ZIP to GitHub Exporter | Push the contents of a ZIP file from Google Drive into a new or existing GitHub repository. |
| 🔍 Identifying LLM "Tells" | N-gram analysis to identify words and phrases statistically more likely in AI-generated text vs. human writing. |
| 💻 Querying a Codebase with Vertex AI RAG | Index code files from a public GitHub repo and ask questions about the codebase using Vertex AI RAG Engine. |
| 🛍️ Product Data Enrichment with Vertex AI | Enrich product description metadata using Generative AI to improve engagement and conversion rates. |
| 📈 Causal Inference with Vertex AI Forecasting | Estimate the effect of an intervention using tfcausalimpact and Vertex AI AutoML Forecasting. |
| 🏥 Medical Imaging Pipeline | Pre-process DICOM medical images, create an AutoML model, and deploy it to an endpoint using a Vertex AI pipeline. |
| 🏥 Medical Imaging Custom Training | Train a TensorFlow model on medical images using a Vertex AI managed dataset. |
| 🧠 TensorFlow SHAP Explainability | Build a tf.keras model and analyze feature importances using the SHAP library. |
| 📰 20 Newsgroups AutoML Import | Download and transform the 20 newsgroups dataset into a CSV for Google Cloud AutoML Natural Language. |
| 💬 Google Cloud Natural Language API | Entity extraction, text classification, sentiment analysis, and syntax analysis with the Natural Language API. |