Alternatives to Comet.ml logo

Alternatives to Comet.ml

Comet.ml, TensorFlow, scikit-learn, Keras, and PyTorch are the most popular alternatives and competitors to Comet.ml.
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What is Comet.ml and what are its top alternatives?

Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.
Comet.ml is a tool in the Machine Learning Tools category of a tech stack.

Comet.ml alternatives & related posts

Comet.ml logo

Comet.ml

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Track, compare and collaborate on Machine Learning experiments
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Comet.ml
TensorFlow logo

TensorFlow

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Open Source Software Library for Machine Intelligence
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related TensorFlow posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 6 upvotes · 558.7K views
atUber TechnologiesUber Technologies
TensorFlow
TensorFlow
Keras
Keras
PyTorch
PyTorch

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details‚ÄĒfor instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA‚Äôs CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo‚Äôs deep learning toolkit which makes it easier to start‚ÄĒand speed up‚ÄĒdistributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

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StackShare Editors
StackShare Editors
| 4 upvotes · 80.8K views
atUber TechnologiesUber Technologies
Cassandra
Cassandra
Apache Spark
Apache Spark
TensorFlow
TensorFlow

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

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scikit-learn logo

scikit-learn

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Easy-to-use and general-purpose machine learning in Python
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scikit-learn
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Comet.ml
Keras logo

Keras

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Deep Learning library for Theano and TensorFlow
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Keras
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Comet.ml

related Keras posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 6 upvotes · 558.7K views
atUber TechnologiesUber Technologies
TensorFlow
TensorFlow
Keras
Keras
PyTorch
PyTorch

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details‚ÄĒfor instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA‚Äôs CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo‚Äôs deep learning toolkit which makes it easier to start‚ÄĒand speed up‚ÄĒdistributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more
PyTorch logo

PyTorch

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A deep learning framework that puts Python first
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PyTorch
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Comet.ml

related PyTorch posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 6 upvotes · 558.7K views
atUber TechnologiesUber Technologies
TensorFlow
TensorFlow
Keras
Keras
PyTorch
PyTorch

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details‚ÄĒfor instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA‚Äôs CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo‚Äôs deep learning toolkit which makes it easier to start‚ÄĒand speed up‚ÄĒdistributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more
ML Kit logo

ML Kit

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Machine learning for mobile developers (by Google)
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    CUDA logo

    CUDA

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    It provides everything you need to develop GPU-accelerated applications
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      Comet.ml
      TensorFlow.js logo

      TensorFlow.js

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      Machine Learning in JavaScript
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      Comet.ml
      H2O logo

      H2O

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      H2O.ai AI for Business Transformation
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        PredictionIO logo

        PredictionIO

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        Open Source Machine Learning Server
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        NLTK logo

        NLTK

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        It is a leading platform for building Python programs to work with human language data
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          Caffe2 logo

          Caffe2

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          Open Source Cross-Platform Machine Learning Tools (by Facebook)
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          Caffe2
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          Comet.ml
          .NET for Apache Spark logo

          .NET for Apache Spark

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          Makes Apache Spark‚ĄĘ Easily Accessible to .NET Developers
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            Kubeflow logo

            Kubeflow

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            Machine Learning Toolkit for Kubernetes
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              Pipelines logo

              Pipelines

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              Machine Learning Pipelines for Kubeflow
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                Microsoft Cognitive Services logo

                Microsoft Cognitive Services

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                APIs, SDKs, and services available to help developers build intelligent applications
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                  Stan logo

                  Stan

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                  A Probabilistic Programming Language
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                    MLflow logo

                    MLflow

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                    An open source machine learning platform
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                      Ludwig logo

                      Ludwig

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                      A code-free deep learning toolbox, by Uber
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                        Theano logo

                        Theano

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                        Define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently
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