Machine learning is a rapidly evolving field that has shown incredible promise in revolutionizing various industries, from healthcare to finance and beyond. However, conducting machine learning experiments is a complex and iterative process that involves numerous experiments with different datasets, models, and hyperparameters. This process can be time-consuming, and it's often challenging to keep track of all the experiments and their outcomes. Machine learning experiment tracking is a crucial tool that enables researchers to streamline the experimentation process, improve model performance, and ensure reproducibility. By tracking experiments, researchers can analyze the results obtained from different configurations systematically, select the best datasets and hyperparameters for their models, and collaborate with others in the field. In this article, we provide a comprehensive introduction to machine learning experiment tracking, covering the essential concepts, best practices, and tools available for implementing an effective experiment-tracking system.

At first glance, building and deploying machine learning models looks a lot like writing code. Tracking experiments in an organized way helps with all of these core issues. Weights and Biases (wandb) is a simple tool that helps individuals to track their experiments -- I talked to several machine learning leaders of different size teams about how they use wandb to track their experiments. The essential unit of progress in an ML project is an experiment, so most people track what they're doing somehow -- generally I see practitioners start with a spreadsheet or a text file to keep track of what they're doing. Spreadsheets and docs are incredibly flexible -- what's wrong with this approach?

At first glance, building and deploying machine learning models looks a lot like writing code. Tracking experiments in an organized way helps with all of these core issues. Weights and Biases (wandb) is a simple tool that helps individuals to track their experiments -- I talked to several machine learning leaders of different size teams about how they use wandb to track their experiments. The essential unit of progress in an ML project is an experiment, so most people track what they're doing somehow -- generally I see practitioners start with a spreadsheet or a text file to keep track of what they're doing. Spreadsheets and docs are incredibly flexible -- what's wrong with this approach?

Once you have a number of models logged, you have way more dimensions to examine than can be looked at all at once. One powerful visualization tool we've discovered is the parallel coordinates chart. Here each line is an individual experiment and each column is an input hyperparameter or an output metric. I've highlighted the top accuracy runs and it shows quite clearly that across all of my experiments that I've selected, high accuracy comes from low dropout values. Aggregate metrics are good, but it is essential to look at specific examples.