MLOps Blog

15 Best Tools for ML Experiment Tracking and Management

7 min
4th October, 2023

While working on a machine learning project, getting good results from a single model-training run is one thing. But keeping all of your machine learning experiments well organized and having a process that lets you draw valid conclusions from them is quite another. 

The answer to these needs is ML experiment tracking. In machine learning, experiment tracking is the process of saving all experiment-related information that you care about for every experiment you run. 

ML teams implement experiment tracking in different ways, may it be by using spreadsheets, GitHub, or self-built platforms. Yet, the most effective option is to do it with tools designed specifically for tracking and managing ML experiments.

In this article, we overview and compare the 15 best tools that will allow you to track and manage your ML experiments. You’ll get to know their main features and see how they are different from each other. Hopefully, this will help you evaluate them and choose the right one for your needs. 

How to evaluate an experiment tracking tool? 

There’s no one answer to the question “what is the best experiment tracking tool?”. Your motivation and needs may be completely different when you work individually or in a team. And, depending on your role, you may be looking for various functionalities. 

If you’re a Data Scientist or a Researcher, you should consider: 

  • If the tool comes with a web UI or it’s console-based;
  • If you can integrate the tool with your preferred model training frameworks;
  • What metadata you can log, display, and compare (code, text, audio, video, etc.);
  • Can you easily compare multiple runs? If so, in what format – only table, or also charts;
  • If organizing and searching through experiments is user-friendly;
  • If you can customize metadata structure and dashboards;
  • If the tool lets you track hardware consumption;
  • How easy it is to collaborate with other team members – can you just share a link to the experiment or you have to use screenshots as a workaround?

As an ML Engineer, you should check if the tool lets you: 

  • Easily reproduce and re-run experiments;
  • Track and search through experiment lineage (data/models/experiments used downstream); 
  • Save, fetch, and cache datasets for experiments;
  • Integrate it with your CI/CD pipeline;
  • Easily collaborate and share work with your colleagues.

Finally, as an ML team lead, you’ll be interested in:

  • General business-related stuff like pricing model, security, and support;
  • How much infrastructure the tool requires, how easy it is to integrate it into your current workflow;
  • Is the product delivered as commercial software, open-source software, or a managed cloud service?
  • What collaboration, sharing, and review feature it has. 

I made sure to keep these motivations in mind when reviewing the tools that are on the market. So let’s take a closer look at them. 

The best tools for ML experiment tracking and management

Before we dig into each tool, here’s a high-level comparison of features and integrations of the 15 best experiment tracking and management tools.

Note: This table was last updated on 20 December 2021. Some information may be outdated today. See some incorrect info? Let us know, and we’ll update it.

PDF version
neptune.ai
Weights & Biases
Comet
Sacred & Omniboard
MLflow
TensorBoard
Guild AI
Polyaxon
ClearML
Valohai
Pachyderm
Kubeflow
Verta.ai
SageMaker Studio
DVC Studio
PDF version
neptune.ai
Weights & Biases
Comet
Sacred & Omniboard
MLflow
TensorBoard
Guild AI
Polyaxon
ClearML
Valohai
Pachyderm
Kubeflow
Verta.ai
SageMaker Studio
DVC Studio
Focus
Metadata Storage, Experiment Tracking, Model Registry
Experiment Management
Experiment Management
Experiment Management
Entire Lifecycle
Experiment Management
Experiment Management
Experiment Management
Experiment Management
Entire Lifecycle
Entire Lifecycle
Run Orchestration
Entire Lifecycle
Entire Lifecycle
Data Versioning
Price
  • Individual: Free
  • Academia: Free
  • Team: Paid
  • Individual: Free (+ usage above free quota)
  • Academia: Free
  • Team: Paid
  • Individual: Free (+ usage above free quota)
  • Academia: Free
  • Team: Paid
Free
Free
Free
Free

Free or paid, depending on the plan

NA
Free
  • Open-source: free
  • Hosted: paid
  • DVC: free
  • DVC Studio: free or paid, depending on the plan
Standalone component or a part of a broader ML platform?
Standalone component. ML metadata store that focuses on experiment tracking and model registry
Standalone component
Stand-alone tool with community, self-serve and managed deployment options
Omniboard is a web dashboard for the Sacred machine learning experiment management tool
Open-source platform which offers four separate components for experiment tracking, code packaging, model deploymnet, and model registry
Open source tool which is a part of the TensorFlow ecosystem
Stand-alone open-source platform
Standalone tool
Standalone open-source platform
Standalone component
Standalone tool
Part of the Kubernetes environment
Standalone component
Part of the AWS SageMaker ecosystem
Standalone component
Commercial software, open-source software, or a managed cloud service?
Managed cloud service
Managed cloud service
Managed cloud service
Open-source
The standalone product is open-source, while the Databricks managed version is commercial
TensorBoarsd is open-source, while TensorBoard.dev is available as a free managed cloud service
Open-source
Offers both an open-source community plan, and various managed cloud options
Available both as an open-source platform, and a managed cloud service
Managed Cloud Service
Base package is open-source, with an enterprise grade commercial offering available
The base product is open source, with managed distributions made available by cloud providers
Available both as an open-source platform, and a managed cloud service
Managed Cloud Service
Open-source and managed cloud service
Hosted version or deployed on-premise?
Tracking is hosted on a managed server, and can also be deployed on-premises and in a public/private cloud server
Yes
Yes, you can deploy Comet on any cloud environment or on-premise
Can be deployed both on-premises and/or on the cloud, but has to be self-managed
Tracking is hosted on a local/remote server (on-prem or cloud). Is also available on a managed server as part of the Databricks platform
TensorBoard is hosted locally. TensorBoard.dev is available on a managed server as a free service
Can be deployed both on-premises and/or on the cloud, but has to be self-managed
Can be hosted both on-prem and on the cloud
Available both on-prem and on cloud
Available both on-prem and on cloud
Can be hosted both on-prem and on the cloud
Almost all popular cloud providers maintain their own distribution of Kubeflow. It can also be installed on-premises manually. Read about the different installation options available here
Available both on-prem and on cloud
AWS Sagemaker is available only as a fully managed cloud service
Tracking is hosted on a managed server, and can also be deployed on-premises and in a public/private cloud server
How much do you have to change in your training process?
Minimal. Just a few lines of code needed for tracking
Minimal. Just a few lines of code needed for tracking
Minimal. Few lines of code needed for tracking
Minimal. Only a few lines of code need to be added
Minimal. Few lines of code needed for tracking
Minimal if already using the TensorFlow framework, else significant
No code change required for basic tracking
Extensive code and infrastructure changes required
Minimal. Just a few lines of code needed for tracking.
No code change required. Some additional workflow steps are added though
Extensive code and infrastructure changes required
Extensive code and infrastructure changes required
Minimal. Few lines of code needed for tracking
AWS Application Migration Service lets you lift-and-shift your code to AWS without any change required. Minimal change required to migrate Jupyter notebooks from local to Sagemaker Studio
Minimal. Just a few lines of code needed for tracking
Web UI or console-based?
Web UI
Web UI
Web UI
Web UI
Web UI
Web UI
Both CLI and Web UI
Both Web UI and CLI
Web UI
Both Web UI and CLI
Both Web UI and CLI
Web UI
Web UI
Both web UI and CLI
Both web and console UI
Log and display of metadata
– Dataset
Limited
Limited
Limited
Limited
Limited
Limited
Limited
Limited
Limited
Limited
Limited
Limited
Limited
– Code Versions
Limited
Limited
Limited
Limited
Limited
Limited
– Parameters
– Metrics and losses
Limited
Limited
Limited
Limited
Limited
Limited
Limited
– Images
Limited
N/A
Limited
N/A
– Audio
N/A
– Video
N/A
– Hardware consumption
Limited
Limited
Limited
Limited
Limited
Limited
Limitted
Comparing experiment
– Table format diff
Limited
– Overlayed learning curves
– Code
Limited
Limited
Limited
Limited
Organizing and searching experiments and metadata
– Experiment table customization
Limited
Limited
Limited
Limited
Limited
– Custom dashboards
Limited
Limited
Limited
Limited
Limited
Limited
– Nested metadata structure support in the UI
Limited
Reproducibility and traceability
– One-command experiment re-run
Limited
N/A
N/A
– Experiment lineage
Limited
Limited
Limited
– Environment versioning
N/A
N/A
– Saving/fetching/caching datasets for experiments
Limited
N/A
N/A
Collaboration and knowledge sharing
– User groups and ACL
Only for Teams and Enterprise customers
Only for paid plans
Only for Teams Pro and Enterprise
Only in the managed version
Only available in the commercial version
Only for enterprise customers
Only for enterprise customers
Only in the enterprise edition
Only for enterprise customers
Only for Teams and Enterprise customers
– Sharing UI links with project members
Only in TensorBoard.dev
Limited
N/A
N/A
– Sharing UI links with external people
Only in TensorBoard.dev
Limited
N/A
N/A
– Commenting
N/A
Limited
Integrations
R
Limited
Limited
TensorBoard
Limited
N/A
MLFlow
Limited
N/A
Sacred
N/A
Amazon SageMaker
N/A
Google Colab
Limited
Kubeflow
N/A
Keras
Limited
Tensorflow
Limited
Pytorch
Scikit-Learn
LightGBM
XGBoost
fastai
No native integrations, only examples available
skorch
PyTorch Lightning
PyTorch Ignitet
Limited
Catalyst
Optuna
Scikit-Optimize
Limited
RayTune
Huggingface
Prophet
Amazon’s own Prophet distribution
PDF version

1. Neptune

Neptune is a metadata store for any MLOps workflow. It was built for both research and production teams that run a lot of experiments. It lets you monitor, visualize, and compare thousands of ML models in one place. 

Neptune supports experiment tracking, model registry, and model monitoring and it’s designed in a way that enables easy collaboration. 

Users can create projects within the app, work on them together, and share UI links with each other (or even with external stakeholders). All this functionality makes Neptune the link between all members of the ML team. 

Neptune is available in the cloud version and can be deployed on-premise. It’s also integrated with 25+ other tools and libraries, including multiple model training and hyperparameter optimization tools.

Main advantages:

2. Weights & Biases

Weight & Biases is a machine learning platform built for experiment tracking, dataset versioning, and model management. For the experiment tracking part, its main focus is to help Data Scientists track every part of the model training process, visualize models, and compare experiments.

W&B is also available in the cloud and as an on-premise tool. In terms of integrations, Weights & Biases support multiple other frameworks and libraries including Keras, PyTorch environment, TensorFlow, Fastai, Scikit-learn, and more. 

Main advantages:

  • A user-friendly and interactive dashboard that’s a central place of all experiments in the app. It allows users to organize and visualize results of their model training process;
  • Hyperparameter search and model optimization with W&B Sweeps;
  • Diffing and deduplication of logged datasets.