Introducing Comet.ml Project Visualizations
Compare across your model iterations efficiently with rich visualizations to identify your champion model
At Comet.ml, we believe that machine learning should be highly iterative, collaborative, and reproducible.
Comet.ml allows data science teams to automatically track their datasets, code changes, experimentation history and models creating efficiency, transparency, and reproducibility. One of our most popular features have been our live experiment tracking charts — with Project Visualizations, we’ve extended our rich visualizations across experiments to help users compare across many experiment iterations.
Introducing Project Visualizations
Project Visualizations were born out of a user need for both focus and context during model iteration.
Focus — How do I identify my best performing (champion model) quickly among 1,000 runs? Which specific hyperparameter set + model configuration gave me the highest accuracy?
Context—In my 1,000 runs, what kind of parameter space was I searching in? Should I expand it to incorporate more + different learning rates, epochs, batch sizes, etc…? When I share these results with my manager, how do I provide enough background on the approaches I tried to arrive at this best result?
To answer these questions and to build truly robust machine learning models, data scientists and machine learning engineers need to:
- have a consistent and thorough record of past work
- have time and resources for faster iteration cycles
- easily share results and collaborate to generate insights
Project Visualizations makes all of those goals and much more possible. See what kind of visualizations are available and an example on how to generate them below 👇🏼
A Wide Range of Visualization Options
Project-level visualizations allow you to compare, explore, and analyze across all your Machine Learning experiments. The visualization options we provide include:
- Line Charts — compare and detect differences in your models’ training process.
2. Bar Charts — easily identify top performing models.
3. Parallel Coordinates Chart — visualize an n-dimensional parameter space. Incredibly useful to explore your hyper parameter space and to identify the most effective parameter combination.
Enjoyed this article? Here are some other articles you might find interesting:
- Comet.ml Release Notes — updated daily with new features and fixes!
- Real-Time Model Performance Visualizations with Comet.ml
- Building Reliable Machine Learning Models with Cross Validation
