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Metrics are the numbers a run produces: error scores, accuracy, a loss that falls during training. Unlike parameters, a metric can be logged many times, so MLflow keeps its whole history and draws it as a chart. This guide logs single values, a batch, and a metric at ten steps, reads the history back, and shows what happens when a value isn't a number.
Parameters are the settings that shape a run: the number of trees, a learning rate, which features you used. Logging them is what lets you answer "what was different about the good run?" later. This guide logs parameters one at a time and in a batch, shows how MLflow stores them, and runs into the one rule people trip over: a parameter can't change once it's logged.
A run is one execution of your code that MLflow records: its settings, results and files. Everything you log goes into the active run, so how you start and end runs decides where your data lands. This guide starts runs four ways, names them, checks their status, and shows what MLflow does when your code fails halfway, with real output from MLflow 3.18.0.
An MLflow experiment is a folder for related runs: one per project, or one per question you're trying to answer. Every run belongs to exactly one experiment. This guide creates experiments two ways, reads their ID and artifact location, shows the error you get for a duplicate name, and finds them in the UI, with real output from MLflow 3.18.0.
The tracking URI tells your code where MLflow records go. Get it wrong and nothing fails: MLflow quietly writes your runs somewhere else, and your UI stays empty. This guide shows the two ways to set it, which one wins, how to check it, and exactly what MLflow 3.18 does when you don't set it, with real output.
The MLflow UI is where your runs become useful: every setting, score and chart in one place, searchable and comparable. MLflow 3.18's UI serves two audiences, AI apps and classic model training, and opens in the AI view by default. This tour switches it to model training and walks through the pages you'll use most: experiments, the runs table, and a run's own page.
MLflow installs like any Python package, but it brings a lot with it: a web server, a database layer and the most common data science libraries. Installing it into a uv project keeps all of that inside the project and records the exact versions. This guide installs MLflow 3.18.0, checks it, and explains which of MLflow's three packages you need.
mean_squared_error(y_true, y_pred, squared=False) is how many tutorials calculate RMSE. In scikit-learn 1.9.1 it fails with TypeError: got an unexpected keyword argument 'squared': the argument was deprecated in scikit-learn 1.4 and has since been removed. root_mean_squared_error() does the same calculation.
Azure OpenAI provides access to powerful AI models through Microsoft Azure cloud platform. This comprehensive guide will walk you through creating an Azure OpenAI resource and deploying AI models, enabling you to integrate advanced AI capabilities into your applications.
