tracking

Complete guide to experiment tracking with MLflow.

Skills
AI research
4

What it does and how to use it

A skill that teaches Claude Code to do ai research tasks well. It is packaged knowledge the model uses whenever it is relevant.

Install the skill (command above).
In Claude Code, describe what you need - the skill turns on by itself when the context fits.
No commands to memorize: you talk in plain language and the skill does the rest.

Install (Claude Code)

mkdir -p .claude/skills/tracking && curl -fsSL https://doutorai.pt/data/lib/skills__tracking.md -o .claude/skills/tracking/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

# Comprehensive Tracking Guide

Complete guide to experiment tracking with MLflow.

## Table of Contents
- Logging Parameters
- Logging Metrics
- Logging Artifacts
- Logging Models
- Autologging
- Runs and Experiments
- Searching and Comparing

## Logging Parameters

### Basic Parameter Logging

```python
import mlflow

with mlflow.start_run():
    # Single parameter
    mlflow.log_param("learning_rate", 0.001)
    mlflow.log_param("batch_size", 32)
    mlflow.log_param("optimizer", "Adam")

    # Multiple parameters at once
    mlflow.log_params({
        "epochs": 50,
        "dropout": 0.2,

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