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skip to Main Content * Enterprise * Products * Experiment Management * Artifacts * Model Registry * Model Production Monitoring * Customers * Learn * Docs * Resources * Blog * Heartbeat * Deep Learning Weekly * Pricing * Company * About Us * News and Events * Events * Press Releases * Careers * Contact Us * Leadership * Login * Create a Free Account LESS FRICTION, MORE ML Comet’s machine learning platform integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring. Try Comet FreeTalk to Us Watch brief demo LESS FRICTION, MORE ML Comet’s machine learning platform integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring. Watch brief demo Try Comet FreeTalk to Us TRUSTED BY THE MOST INNOVATIVE ML TEAMS TRUSTED BY THE MOST INNOVATIVE ML TEAMS previous slidenext slide MONITOR AND MANAGE MODELS, FROM SMALL TEAMS TO MASSIVE SCALE EASY TO INTEGRATE WITH ANY TRAINING ENVIRONMENT Add two lines of code to your notebook or script and automatically start tracking code, hyperparameters, metrics, and more, so you can compare and reproduce training runs. Experiment Management PythonJavaR 1 from comet_ml import Experiment 2 3 # Initialize the Comet logger 4 experiment = Experiment() TRACK AND SHARE TRAINING RUN RESULTS IN REAL TIME Comet’s ML platform gives you visibility into training runs and models so you can iterate faster. Experiment Management BUILD YOUR OWN TAILORED, INTERACTIVE VISUALIZATIONS In addition to the 30+ built-in visualizations Comet provides, you can code your own visualizations using Plotly and Matplotlib. TRACK AND VERSION DATASETS AND ARTIFACTS Knowing what data was used to train a model is a key part of the MLOps lifecycle. Comet Artifacts allows you to track data by uploading directly to Comet’s machine learning platform or by storing a reference to it. Comet Artifacts MANAGE YOUR MODELS AND TRIGGER DEPLOYMENTS Comet Model Registry allows you to keep track of your models ready for deployment. Thanks to the tight integration with Comet Experiment Management, you will have full lineage from training to production. Comet Model Registry MONITOR YOUR MODELS IN PRODUCTION The performance of models deployed to production degrade over time, either due to drift or data quality. Use Comet’s machine learning platform to identify drift and track accuracy metrics using baselines automatically pulled from training runs. Comet Model Production Monitoring EASY INTEGRATION Add two lines of code to your notebook or script and automatically start tracking code, hyperparameters, metrics, and more. Try a Live Notebook EXPERIMENT MANAGEMENT PYTORCH PYTORCH LIGHTNING HUGGING FACE KERAS TENSORFLOW SCIKIT-LEARN XGBOOST ANY FRAMEWORK MODEL MONITORING ANY FRAMEWORK from comet_ml import Experiment import torch import torch.nn as nn # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Create your model class class RNN(nn.Module): #... Define your Class # 3. Train and test your model while logging everything to Comet with experiment.train(): ...Train your model and log metrics experiment.log_metric("accuracy", correct / total, step = step) from comet_ml import Experiment import pytorch_lightning as pl # 1. Create your Model class PyTorchLightningModel(pl.LightningModule): def __init__(self, hparams): ...Define your model Class # 2. Initialize CometLogger comet_logger = CometLogger() # 3. Train your model trainer = pl.Trainer( ...configs, logger=[comet_logger] ) trainer.fit(model) from comet_ml import Experiment from transformers import Trainer # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Train your model trainer = Trainer( model=model, ... ) trainer.train() from comet_ml import Experiment from tensorflow import keras # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Define your model ...Define your Model # 3. Train your model model.fit( x_train, y_train, validation_data=(x_test, y_test), ) from comet_ml import Experiment import tensorflow as tf # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Define and train your model ...Build your model model.fit(...) # 3. Log additional model metrics and params exp.log_parameters(params) exp.log_metric('custom_metric', 0.95) from comet_ml import Experiment import sklearn # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Build your model and fit ...Build your model clf.fit(X_train_scaled, y_train) params = {...} metrics = {...} # 3. Log additional metrics and params experiment.log_parameters(params) experiment.log_metrics(metrics) from comet_ml import Experiment import xgboost as xgb # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Define your model and fit xg_reg = xgb.XGBRegressor(**param) xg_reg.fit( X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], eval_metric="rmse", ) # Utilize Comet in any environment from comet_ml import Experiment # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Model training here # 3. Log metrics or params over time experiment.log_metrics(metrics) # Utilize Comet in any environment from comet_mpm import CometMPM # 1. Create the MPM logger MPM = CometMPM() # 2. Add your inference logic here # 3. Log metrics or params over time MPM.log_event( prediction_id="...", input_features=input_features, output_value=prediction, output_probability=probability, ) EXPERIMENT MANAGEMENT PYTORCH from comet_ml import Experiment import torch import torch.nn as nn # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Create your model class class RNN(nn.Module): #... Define your Class # 3. Train and test your model while logging everything to Comet with experiment.train(): ...Train your model and log metrics experiment.log_metric("accuracy", correct / total, step = step) PYTORCH LIGHTNING from comet_ml import Experiment import pytorch_lightning as pl # 1. Create your Model class PyTorchLightningModel(pl.LightningModule): def __init__(self, hparams): ...Define your model Class # 2. Initialize CometLogger comet_logger = CometLogger() # 3. Train your model trainer = pl.Trainer( ...configs, logger=[comet_logger] ) trainer.fit(model) HUGGING FACE from comet_ml import Experiment from transformers import Trainer # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Train your model trainer = Trainer( model=model, ... ) trainer.train() KERAS from comet_ml import Experiment from tensorflow import keras # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Define your model ...Define your Model # 3. Train your model model.fit( x_train, y_train, validation_data=(x_test, y_test), ) TENSORFLOW from comet_ml import Experiment import tensorflow as tf # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Define and train your model ...Build your model model.fit(...) # 3. Log additional model metrics and params exp.log_parameters(params) exp.log_metric('custom_metric', 0.95) SCIKIT-LEARN from comet_ml import Experiment import sklearn # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Build your model and fit ...Build your model clf.fit(X_train_scaled, y_train) params = {...} metrics = {...} # 3. Log additional metrics and params experiment.log_parameters(params) experiment.log_metrics(metrics) XGBOOST from comet_ml import Experiment import xgboost as xgb # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Define your model and fit xg_reg = xgb.XGBRegressor(**param) xg_reg.fit( X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], eval_metric="rmse", ) ANY FRAMEWORK # Utilize Comet in any environment from comet_ml import Experiment # 1. Define a new experiment exp = Experiment(project_name="YOUR PROJECT") # 2. Model training here # 3. Log metrics or params over time experiment.log_metrics(metrics) MODEL MONITORING ANY FRAMEWORK # Utilize Comet in any environment from comet_mpm import CometMPM # 1. Create the MPM logger MPM = CometMPM() # 2. Add your inference logic here # 3. Log metrics or params over time MPM.log_event( prediction_id="...", input_features=input_features, output_value=prediction, output_probability=probability, ) AN EXTENSIBLE, FULLY CUSTOMIZABLE MACHINE LEARNING PLATFORM Comet’s ML platform supports productivity, reproducibility, and collaboration, no matter what tools you use to train and deploy models: managed, open source, or in-house. Use Comet’s platform on cloud, virtual private cloud (VPC), or on-premises. Manage and version your training data, track and compare training runs, create a model registry, and monitor your models in production—all in one platform. MOVE ML FORWARD—YOUR WAY Run Comet’s ML platform on any infrastructure. Bring your existing software and data stack. Use code panels to create visualizations in your preferred user interfaces. * Before Comet * After Comet BEFORE COMET AFTER COMET INFRASTRUCTURE AN ML PLATFORM BUILT FOR ENTERPRISE, DRIVEN BY COMMUNITY Comet’s ML platform is trusted by innovative data scientists, ML practitioners, and engineers in the most demanding enterprise environments. "Comet has aided our success with ML and serves to further ML development within Zappos.” 10% reduction in order returns due to size Kyle Anderson Director of Software Engineering "Comet offers the most complete experiment tracking solution on the market. It’s brought significant value to our business." Service for millions of customers Olcay Cirit Staff Research and Tech Lead “Comet enables us to speed up research cycles and reliably reproduce and collaborate on our modeling projects. It has become an indispensable part of our ML workflow.” Developing NLP tools for thousands of researchers Victor Sanh Machine Learning Scientist "None of the other products have the simplicity, ease of use and feature set that Comet has." Developing speech and language algorithms Ronny Huang Research Scientist View Case Studies "After discovering Comet, our deep learning team’s productivity went up. Comet is easy to set up and allows us to move research faster." Building speech recognition with deep learning Guru Rao Head of AI "We can seamlessly compare and share experiments, debug and stop underperforming models. Comet has improved our efficiency." Pioneering family history research Carol Anderson Staff Data Scientist ENTERPRISE USER "Comet has aided our success with ML and serves to further ML development within Zappos.” 10% reduction in order returns due to size Kyle Anderson Director of Software Engineering ENTERPRISE USER "Comet offers the most complete experiment tracking solution on the market. It’s brought significant value to our business." Service for millions of customers Olcay Cirit Staff Research and Tech Lead COMMUNITY USER “Comet enables us to speed up research cycles and reliably reproduce and collaborate on our modeling projects. It has become an indispensable part of our ML workflow.” Developing NLP tools for thousands of researchers Victor Sanh Machine Learning Scientist COMMUNITY USER "None of the other products have the simplicity, ease of use and feature set that Comet has." Developing speech and language algorithms Ronny Huang Research Scientist View Case Studies ENTERPRISE USER "After discovering Comet, our deep learning team’s productivity went up. Comet is easy to set up and allows us to move research faster." Building speech recognition with deep learning Guru Rao Head of AI ENTERPRISE USER "We can seamlessly compare and share experiments, debug and stop underperforming models. Comet has improved our efficiency." 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