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Experiment Tracking is a technique for linking variables to the changes that those variables cause in your data. You can test many different combinations of variables—run multiple experiments with weights assigned to each one and see which are the most effective when aggregated together. 

One of the challenges with experiment tracking is choosing the right tool for your team. You have many factors to consider: integrations, training progressions, project management capabilities, pricing and more. So, in this article, we’re going to explore:

  • Data Version Control (DVC), 
  • Best Alternatives to Data Version Control (DVC),
  • Comparison of Experiment Tracking Tools

To learn more about  Experiment Tracking, check – ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

Data Version Control (DVC)

DVC is an open-source platform for machine learning projects. DVC helps data scientists and developers with data versioning, workflow management, and experiment management. DVC is easily adaptable, users can take advantage of new features while reusing existing ones. 

DVC Summary

  • Multi-language and multi-framework support.
  • You can version large amounts of data.

Source: dvc.org

  • DVC is a Git-compatible tool that connects repositories of code together to form one seamless network and allows easy collaboration across multiple projects.
  • Sometimes things don’t go as planned. DVC lets you track everything in a reproducible and easily manageable way, so you can save a good amount of time and resources. DVC guarantees reproducibility by maintaining input data, environment variables, code and more.
  • DVC is a lightweight, open-source tool which is easily adaptable with multiple languages and frameworks, but DVC is also new, and you might find it hard to customize.
  • DVC can process large amounts of data but provides limited features.
  • In some cases, there might be scalability issues for large numbers of experiments

DVC isn’t for everyone. So, let’s take a look at some good alternatives to DVC:

Neptune is a metadata store for MLOps, developed for research and production teams. It gives you a central hub to log, store, display, organize, compare, and query all metadata generated during the machine learning lifecycle. Researchers and engineers use Neptune for experiment tracking and model registry to control their experimentation and model development. 

Neptune Summary

Neptune makes it easier for teams to organize and manage machine learning projects. Its intuitive UI lets you organize runs in groups, save custom dashboard views, and quickly share them with your team.

  • Log and display all metadata types including parameters, model weights, media files etc.
  • Easily Collaborate and supervise projects.
  • Intuitive UI with a lot of capabilities to organize runs in groups.
  • Compare insights and parameters.
  • Automatically record the code, environment, parameters, model binaries, and much more.
  • Track experiments that are executed in scripts, notebooks and on any infrastructure.
  • Extensive experiment tracking and visualization capabilities.
  • You can use a hosted app to avoid all the trouble of maintaining yet another tool or deploy it on your infrastructure for maximum control.
  • You can monitor the hardware for your experiment runs automatically. Examine the amount of GPU/CPU and memory your model training runs consume.
  • Neptune offers a Python client library that lets you log and keep track of any metadata type in their ML experiments whether those run in Python scripts, Jupyter Notebooks, Amazon SageMaker Notebooks, or Google Colab.

Pricing 

  • Free for Individuals 
  • Team Research: $0
  • Team: from $49 (Team trial available)
  • Enterprise: starts from $499
  • Learn More: Neptune  

See differences between Neptune vs DVC – Which tool is better (for experiment tracking)


Weights & Biases (WandB) is a platform that provides machine learning tools for researchers and deep learning teams. WandB helps you with experiment tracking, dataset versioning, and model management. WandB lets you easily track, compare, version and visualize your machine learning and deep learning experiments. 

The best part about WandB is that you can access your training model and results on desktop as well as on your mobile. The lightweight collaborative system lets you share and manage your projects easily and with good documentation.

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Source: wandb.ai

WandB Summary

  • It’s easy to use, with a good UI for users to visualize, compare, and organize their experiments into interactive graphs and tables.
  • You can visualize CPU and GPU usage.
  • You can store files and datasets on WandB or on your local storage.
  • Collaborate, easily share, and create a project community with teams.
  • Easily debug audio, video, images and 3D objects.
  • Automatically version logged datasets.
  • Open-source integrations.

Pricing 

  • Basic Plan – Free for Individuals. 
  • Standard Plan – $35.00/mo.
  • Enterprise – Contact Sales Team for Details.

See differences between Weights & Biases vs Neptune.


Comet is a cloud-based meta machine learning platform where developers can track, compare, analyze and optimize experiments. Comet provides real-time stats and graphs about your experiments. 4exnCoyB7ResnqPHcSGQViMzqoTxGie4uB0W0O74IUoySpoV9hpaOR4bvzkQe7iWH2ZZ8Cqsxmugt6ZaReXrN7QpY1n9 83e5FGEzEf8nweKJ6GgzPmguE ebKaVNguaZg1qp0e=s0 |

Source: comet.ml

Comet Summary

  • The integration is quick, just a few lines of code and you can start tracking your ML Experiments without any library.
  • Compare your experiments easily including code, metrics, predictions, insights and a lot more. 
  • Debug and monitor your models, get alerts when something is wrong with your experiments.
  • Easy and productive collaboration platform for data scientists as well as business stakeholders. 
  • Custom visualizations for your experiments and data. 
  • Works automatically with both notebooks and scripts.

Pricing 

  • Basic Plan – Free for Individuals. 
  • Teams – $179 per user/month.
  • Teams Pro – $249 per user/month.
  • Enterprise – Contact Comet for Details.
  • See Comet ML for more information

See differences between Comet and Neptune


MLFlow is an open-source tool for managing the machine learning lifecycle. It helps data scientists and developers with experiments, deployment and model registry. It can work with multiple ML libraries and tools.

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Source: databricks.com/blog

MLFlow Summary

  • It works with multiple machine learning libraries, languages, or any existing code. 
  • MLflow has four main features – tracking, projects, models, and registry. 
  • You can record and query your code and data experiments with MLflow tracking.
  • MLflow projects include code that is reusable and reproducible. It also comes with an API and a command-line tool for ML and data science tasks.
  • Different types of ML models can be deployed using MLFlow. Model is stored as a directory that contains any number of files.

Verta AI provides a platform to track, collaborate, deploy and monitor your machine learning experiments. Verta AI lets you version, manage, analyze, share, govern, deploy, and a lot more. It ensures high-quality operations with consistent performance and scalability.

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Source: verta.ai

Verta AI Summary

  • Supports top open-source frameworks and platforms.
  • Organize work with different attributes.
  • Intuitive user interface.
  • Model reproducibility using code, variables, data and configuration.
  • Easily share and collaborate your experiments with teams.
  • Real-time monitoring and logging.

Pricing 

  • Limited Plan – Free 
  • SaaS – Trial Available 
  • Enterprise – Contact Support 
  • See Verta AI to learn more

Kubeflow is the open-source machine learning tool for Kubernetes. Kubeflow provides detailed and powerful tracking. Kubeflow is not 100% focused on experiment tracing but it does have features like data versioning, model versioning, resource monitoring and several more. 

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Source: kubeflow.org/docs

Kubeflow Summary

  • Reproducible, portable deployments on diverse infrastructure.
  • Open-source, integrates with many frameworks and platforms.
  • Kubeflow is an excellent fit for Kubernetes users.
  • It’s scalable and has a lot of flexibility when it comes to hyperparameter adjustment.
  • Visit Kubeflow to learn more.

See differences between Kubeflow and Neptune and check Kubeflow alternatives


Polyaxon is a specialized application for managing the machine learning lifecycle as well as facilitating ML team cooperation. Polyaxon is for data scientists, architects, team leaders and executives. It provides a wide range of products (like Tracking, Orchestration, Optimization, Insights, Model management, Collaboration, and more).

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Source: github.com/polyaxon

Polyaxon Features 

  • Polyaxon allows you to track essential model metrics, hyperparams, visualisations, artefacts, resources, as well as version control code and data automatically.
  • Compare, filter and search to get better insights into your experiments.
  • Lightweight integration, get started with a few lines of code.
  • Supports most popular frameworks and tools.
  • The link between code and model is very easy without altering your workflow.
  • Easy team collaboration.
  • You can deploy it on the cloud or on a local machine.
  • Run experiments in parallel and in a distributed way.

Pricing 

  • Starter Plan- $300/per month
  • Platform Plan-  – $450/per month
  • Business Plan – $1200/per month
  • Enterprise – Contact Support
  • See Polyaxon to learn more.

See differences beteen Polyaxon and Neptune


With Amazon SageMaker, you can prepare, build, train, tune, deploy, and manage all your machine learning experiments. It provides an easy to use interface which makes developer and data scientist jobs much easier. If you’re already using AWS, then Sagemaker Studio will be best for you, as it provides good integration support for all the AWS products. 

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Source: aws.amazon.com/sagemaker/

Sagemaker Studio Summary

  • It works seamlessly with other AWS tools.
  • Easy to use interface.
  • You can track thousands of experiments. 
  • Manage your experiments from A to Z. 
  • It offers built-in algorithms for training and running your experiments. 
  • SageMaker provides a built-in debugger so you can identify and reduce errors. 

Pricing 

  • Free trial for the first two months.
  • After the free trial ends, it’s pay-as-you-go.
  • Visit Sagemaker to learn more.

See differences between SageMaker Studio andvs Neptune.


Guild AI is an open-source ML experiment tracking platform. It’s lightweight and offers a wide range of features that make running, analysing, and optimising machine learning experiments a lot easier.HTn5IvNtr |

Source: guild.ai

Guild AI Summary

  • Guild automatically stores every process of your experiments. 
  • Compare and analyze, get detailed results on your experiments.
  • Easy to get started, can be integrated with any language and library.
  • Works on both GPU-accelerated cloud systems or on your local machine. 
  • Remote training and backup possibility.
  • Visit Guild AI to learn more.

Choosing the right ML experiment tracking tool for your workflow

Choosing the right ML experiment tracking tool for your team can be hard. You have to consider many things: 

  • integrations, 
  • training progressions, 
  • project management capabilities, 
  • pricing, and more. 

So, we’re going to compare open-source, commercial, and platform-specific tools to see which one might be best for your machine learning or deep learning workflow. First, let’s take a look at the things you need to consider while choosing an experiment tracking tool.

What factors to consider?

Tracking: you’ll be tracking many things including hyperparameters, models, code, resources, insights and more. Make sure the tool you choose provides all the things you need for your machine/deep learning project. 

Storage: saving your data and experiments is important. Some tools provide cloud-based storage, while some prefer local storage. 

Visualizations: a good visual representation will help you analyze outcomes easily. It makes complex things easy to understand, on top of that you can represent visual outcomes to your stakeholders. So, make sure the tool you choose has some good visualization features. 

Stability and Scalability: at the enterprise level, you need a tool that is stable and provides easy team collaboration.

Which tool is the best fit for you?

There are numerous tools available to assist in tracking experiments with various features and techniques. In general, the tools can be divided into three categories:

  • open-source, 
  • commercial, and 
  • platform-specific. 

We’ll look at the benefits and drawbacks of these sections, as well as evaluate a few experiment tracking tools.

Open-Source (DVC, MLflow, etc)

Pros

  • Free
  • Can be customized according to your needs
  • Can process large amounts of data
  • Good community support

Cons

  • Lack of expert support
  • Scalability issues
  • Limited Features
  • Sharing and managing issues in the long term

Commercial (Neptune, Comet, etc)

Pros

  • Easy to use and Intuitive UI
  • Expert Support 
  • Good for long term usage and stability
  • Provides more features for your ML experiments

Cons

  • Price might be an issue in some cases
  • Not every tool and framework is supported
  • Limited customization

Platform-Specific (Amazon SageMaker, etc)

Pros

  • Integrates seamlessly with the platform
  • Expert support 

Cons

  • Might require some special infrastructure and depend on APIs
  • Works well only if integrated with the platform
  • Pricing might be higher than other commercial tools

Experiment tracking tools comparison table

Neptune AI MLFlow Comet Ml Kubeflow DVC SageMaker
Pricing Free for Individuals Team Research: $0Team: from $49 (Team trial available)Enterprise: starts from $499 Open Source Basic Plan – Free for Individuals 
Teams – $179 per user/month
Teams Pro – $249 per user/month
Enterprise – NA
Open Source Open Source
DVC Studio offers Different plans for Team and Enterprise.
Pay As You Go
Tools and Framework Integrations R, Tensorflow, Mlflow, Pytorch, and 16 more R, Tensorflow, XGBoost, Pytorch, and 10 more Tensorflow, Scikit-learn, Pytorch, and 6 more R, Tensorflow,Scikit-learn, Pytorch, and 5 more R R, Tensorflow, Keras, Pytorch, and 8 more
Advantages Flexible and works well with other frameworks,
Intuitive UI,
Easy collaboration with team and stakeholders
MLflow is highly customizable,
Fits perfectly for data science workflow,
Works with any ML library or tool.
Real-time stats and graphs,
Easy collaboration with team and stakeholders
Perfect fit for Kubernetes users,
Highly Scalable 
Easily adaptable with multiple language and framework,
Easy to use and customizable
Works well with the platform,
Easy to use interface 
.
Cautions Limited Customization Visualizations are limited,
Sharing experiments might be an issue,
Limited access controls and support for multiple projects
Lack some features for automatic logging,
Limited Customization
Difficult to setup and get started,
Limited features
DVC is new and might not be stable,
Not scalable for large numbers of experiments
Non-AWS users might find sagemaker difficult to use,
Expensive for some
Custom Visualization
Focus  Experiment Tracking for research and production teams Entire Lifecycle Experiment Tracking Run orchestration Data Versioning and Management with limited experiment tracking features Entire Lifecycle

Conclusion

Experiment tracking plays an important role in your machine learning or deep learning journey, so choosing the right platform for your experiments is a crucial part. There are many tools out there, but only a few will fit your workflow. Some companies provide free trials, so you can try them and see if you like their platforms. Hope this article helped you, good luck with your experiments!

References and Recommended Reading

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2. Install Neptune client library
pip install neptune-client
3. Add logging to your script
import neptune.new as neptune

run = neptune.init('Me/MyProject')
run['params'] = {'lr':0.1, 'dropout':0.4}
run['test_accuracy'] = 0.84


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