End-to-End Machine Learning with AI-First Colab and Gemini

Simplifying the ML Lifecycle with AI-First Colab

• 💡 AI-First Colab is presented as a Jupyter notebook environment on Google Cloud, acting as an agentic collaborator to accelerate machine learning tasks.
• 🚀 It offers a minimal setup, browser-based interface, shareable notebooks via Google Drive, and free access to powerful computing resources like GPUs and TPUs.
• 🧠 Powered by Gemini, the AI companion understands code and data state across the notebook, enabling an iterative and collaborative workflow.

Autonomous ML Workflow with Natural Language

• 🎯 The entire machine learning journey, from data ingestion and preparation to model training, evaluation, and prediction, can be performed autonomously using natural language prompts.
• 📊 This allows users to focus on high-level strategy and direction while the AI handles the coding and execution.

Predicting 2026 Winter Olympics Medal Counts

• 🏅 A real-world demo showcases predicting the total medal counts for the 2026 Winter Olympics using data from past games.
• ✍️ A detailed prompt guides Gemini to use data since 1992, incorporate previous Olympic performance, account for host country effects, evaluate on a holdout set, and ensure predictions sum correctly.
• ⚙️ Gemini autonomously handles data loading, preparation, feature engineering (e.g., previous medal counts, host country effect), linear regression modeling with scikit-learn, and evaluation.

Model Evaluation and Prediction Adjustment

• 📈 The model achieved a mean absolute error of approximately 1.6 medals per country on the 2022 holdout set.
• 🧮 Gemini then generates 2026 predictions and adjusts them to meet the specified total medal count, ensuring the sum aligns with the prompt's requirements.

Visualizing Results and Collaborative Development

• 📊 A final prompt generates an interactive plot visualizing the projected 2026 Winter Olympics medal table, highlighting top-performing countries like Norway and Germany.
• ✅ Users have access to the full code and output, encouraging them to check, modify, and build upon the AI's work for the best collaborative outcomes.
• 🛠️ To start, users can open a Colab notebook, find the Gemini icon, and begin interacting with the AI through prompts or suggested commands.