Workflow
How to Build an End-to-End Data Science Workflow with Machine Learning, Interpretability, and Gemini AI Assistance?
In this tutorial, we walk through an advanced end-to-end data science workflow where we combine traditional machine learning with the power of Gemini. We begin by preparing and modeling the diabetes dataset, then we dive into evaluation, feature importance, and partial dependence. Along the way, we bring in Gemini as our AI data scientist to…
A Coding Guide to Build a Functional Data Analysis Workflow Using Lilac for Transforming, Filtering, and Exporting Structured Insights
In this tutorial, we demonstrate a fully functional and modular data analysis pipeline using the Lilac library, without relying on signal processing. It combines Lilac’s dataset management capabilities with Python’s functional programming paradigm to create a clean, extensible workflow. From setting up a project and generating realistic sample data to extracting insights and exporting filtered…
Building AI-Powered Applications Using the Plan → Files → Code Workflow in TinyDev
In this tutorial, we introduce TinyDev class implementation, a minimal yet powerful AI code generation tool that utilizes the Gemini API to transform simple app ideas into comprehensive, structured applications. Designed to run effortlessly in Notebook, TinyDev follows a clean three-phase workflow—Plan → Files → Code—to ensure consistency, functionality, and modular design. Whether building a…
A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and Gemini
In this tutorial, we demonstrate how to build a multi-step, intelligent query-handling agent using LangGraph and Gemini 1.5 Flash. The core idea is to structure AI reasoning as a stateful workflow, where an incoming query is passed through a series of purposeful nodes: routing, analysis, research, response generation, and validation. Each node operates as a…
A Coding Guide Implementing ScrapeGraph and Gemini AI for an Automated, Scalable, Insight-Driven Competitive Intelligence and Market Analysis Workflow
In this tutorial, we demonstrate how to leverage ScrapeGraph’s powerful scraping tools in combination with Gemini AI to automate the collection, parsing, and analysis of competitor information. By using ScrapeGraph’s SmartScraperTool and MarkdownifyTool, users can extract detailed insights from product offerings, pricing strategies, technology stacks, and market presence directly from competitor websites. The tutorial then…
A Step-by-Step Coding Guide to Defining Custom Model Context Protocol (MCP) Server and Client Tools with FastMCP and Integrating Them into Google Gemini 2.0’s Function‑Calling Workflow
In this Colab‑ready tutorial, we demonstrate how to integrate Google’s Gemini 2.0 generative AI with an in‑process Model Context Protocol (MCP) server, using FastMCP. Starting with an interactive getpass prompt to capture your GEMINI_API_KEY securely, we install and configure all necessary dependencies: the google‑genai Python client for calling the Gemini API, fastmcp for defining and…