MLOps Blog

DVC Alternatives For Experiment Tracking

Harshil Patel

6 min

13th November, 2023

Machine Learning Tools

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 this task. You have many factors to consider: integrations, training progressions, project management capabilities, pricing, and more.

In this article, we’re going to explore one such tool that can help with experiment tracking—Data Version Control (DVC). But, we’ll also:

Review the best alternatives to Data Version Control (DVC),
Compare different 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.

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 at the same time 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
Weights & Biases
Comet ML
MLFlow
Verta AI
Kubeflow
Polyaxon
Amazon SageMaker Studio
Guild AI

Neptune

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

Individual: Free
Academia: Free
Team: Paid
Learn more: Neptune pricing

Compare tools

Check differences between Neptune and DVC – Which tool is better for experiment tracking?

Weights & Biases

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.

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

Individual: Free (+ usage above free quota)
Academia: Free
Team: Paid
Learn more: WandB pricing

Compare tools

Check differences between Weights & Biases and Neptune.

Comet ML

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.

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

Individual: Free (+ usage above free quota)
Academia: Free
Team: Paid
Learn more: Comet pricing

Compare tools

See differences between Comet and Neptune

MLFlow

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.

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.

Explore tools

See differences between MLflow and Neptune or check best MLflow alternatives.

Verta AI

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.

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

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.

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.

Explore tools

See differences between Kubeflow and Neptune and check Kubeflow alternatives

Polyaxon

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).

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.

Compare tools

See differences between Polyaxon and Neptune

Amazon SageMaker Studio

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.

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.

Compare tools

See an in-depth comparison between SageMaker Studio and Neptune.

Guild AI

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.

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 experiment tracking tools (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 experiment tracking tools (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 tools (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

MLflow

Comet

Kubeflow

DVC

Sagemaker

Neptune

MLflow

Comet

Kubeflow

DVC

Sagemaker

Pricing

Free for IndividualsrnTeam Research: $0rnTeam: from $49 (Team trial available)rnEnterprise: starts from $499

Open Source

Basic Plan: Free for IndividualsrnTeams: $179 per user/monthrnTeams Pro: $249 per user/monthrnEnterprise: 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, 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!