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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."
Pioneering family history research
Carol Anderson
Staff Data Scientist



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 * Enterprise
 * Products
   * Experiment Management
   * Artifacts
   * Model Registry
   * Model Production Monitoring
 * Customers
 * Learn
   * Docs
   * Resources
   * Blog
 * Pricing
 * Company
   * About Us
   * News and Events
     * Events
     * Press Releases
   * Careers
   * Leadership
   * Contact Us
 * Login
 * Create a Free Account