While working on a machine learning project, getting good results from a single model-training run is one thing. But keeping your machine learning experiments well organized and having a process that lets you draw valid conclusions 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, for example, through spreadsheets, GitHub, or self-built platforms. Yet, the most effective option is using tools designed specifically for tracking and managing ML experiments.
This article reviews and compares the 13 best tools for tracking and managing your ML experiments. You’ll learn their main features and see how they differ. Before diving into the individual tools, we’ll also discuss what to consider when evaluating ML experiment trackers and how to choose the right one for your team.
What should you expect from ML experiment tracking tools?
Machine-learning experiment tracking is the process of saving experiment-related information for every experiment you run. This allows you to analyze experiments, compare models, and ensure reproducible training.
Information you might want to log using an ML experiment tracker includes:
- Anything that you need to replicate an experiment run: Training and data preparation scripts, environment configuration files, the data used for training and evaluation, model and training parameter configurations, as well as any code or notebooks used for evaluation.
- Anything that you need to use the experiment’s outcome in production: Model artifacts, such as model weights, definitions, or binaries, as well as preprocessing components, such as fitted tokenizers or feature scalers.
- Analysis results that enable you to assess and compare experiments: Values for evaluation metrics, plots of training progress and other performance visualizations, example predictions, and data about hardware consumption.
This is a lot of different information in a broad range of formats. ML experiment tracking tools aim to make collecting, cataloging, searching, and retrieving this data easy and convenient.
An experiment-tracking tool consists of three main components:
- A way to store and catalog the metadata and artifacts. Typically, a database and an artifact registry. Some tools rely on an external solution to store large files, just keeping track of a link.
- A client library you can integrate into your model training and evaluation scripts to log metrics and upload files to the experiment tracker.
- A user interface to view the data, often including dashboards, means to find past experiment runs, and ways to collaborate with team members. Many experiment trackers also provide an API to fetch data programmatically, e.g., to re-run an experiment on new data. Some ship with a CLI tool.
ML experiment tracking tools available in the market differ widely – just as the needs of machine-learning teams looking to adopt one of them. Thus, before we turn our attention to specific tools, we’ll discuss what you should consider when choosing an ML experiment tracker.
How to Build an Experiment Tracking Tool [Learnings From Engineers Behind Neptune]
How should you evaluate ML experiment-tracking tools?
There is no one correct answer to the question, “What is the best experiment-tracking tool?”
If you’re a solo data scientist without a budget, an open-source solution you can host on your laptop might be a good fit. If you’re an ML researcher exploring novel models and algorithms, you’ll require the flexibility to integrate with exotic frameworks and track custom metrics. And if you’re managing a department at a Fortune 500, cross-team collaboration and compliance will be first of mind.
Therefore, before you look into individual tools, it’s paramount that you understand your essential requirements.
An experiment tracker has to fit your team’s workflow
Data science teams come in all shapes and sizes.
Some teams are business-oriented, working on analyses and forecasts and producing reports and results. Members often have a business, economics, or statistics background but no software engineering experience. They might be comfortable using Jupyter or R notebooks for data wrangling and model training but prefer a rich UI for most other tasks.
Other teams are staffed with computer scientists who transitioned into ML engineering. Typically, this group likes to interact with tools through CLIs and configure them through code, using UIs only for monitoring purposes. Often, their work’s outcomes are distributed workflows for automatically training models and deploying them as part of a larger application.
Even from these brief and stereotypical descriptions, it’s clear that the teams’ needs differ widely. Some questions to consider:
- Interfaces: Does your team want to interact through a UI? Are your team members comfortable using a CLI? Do you want to retrieve and analyze the collected data through code?
- Customizability: Are the built-in views in the UI sufficient? Does the tool allow you to build the custom dashboards you need now or in the future?
- Integrations: Does the experiment tracker mesh well with the databases and training environments? If you’re organizing your training and experiments through Git and a CI/CD solution, does the tool support this type of workflow?
- Support for projects and experiments: How easy is it to keep track of a large number of experiments or experiment runs? Can you easily re-run an experiment? How much effort is it to create a new project or experiment?
- Access to metadata and artifacts: Does the experiment tracker enable you to find the data you’re looking for? Can you retrieve metadata, models, or analysis results in a way that suits your needs?
An experiment tracker has to work with your machine-learning frameworks and models
Most experiment-tracking tools boast compatibility with the most relevant machine-learning and deep-learning libraries. However, since virtually all experiment trackers allow users to log arbitrary data points, that’s an easy claim to make.
You should investigate whether a tool ships with access to ready-made callbacks or integrations for your ML framework of choice. Some frameworks, such as HuggingFace’s transformers, include third-party integrations themselves.
Aside from the framework, you should also determine what types of models you’ll train and experiment with. A tool that excels for computer vision models might have little to offer for NLP tasks, and time-series data requires a different set of metrics and visualizations than graph learning.
Here are some questions to discuss with your team:
- Frameworks and libraries: What frameworks are you using to create and train your models? Do you have any plans to adopt additional frameworks soon?
- Types of models and tasks: What algorithms and model architectures do you work with? Do you have a predominant machine-learning task, such as classification or text generation? What does a typical model output look like?
- Metadata to log: What metrics do you log? What plots do you typically look at? Do you need to log and analyze images, text, or tables? What about binary, CSV, or JSON files?
- Resource monitoring: Is tracking hardware utilization and resource consumption a concern for you? Do you monitor CPU or GPU load and memory usage during training
Optimizing GPU usage during model training with neptune.ai
An experiment tracker has to meet your collaboration needs
Few machine-learning practitioners work on their own. Most of us are part of a team where we regularly discuss our experiments, develop new ideas to try, or review what our predecessors have tried in the past.
Teams sharing an office can do that on a whiteboard or sit together in front of the same screen. However, many teams work remotely, have collaborators on the other side of the planet, or cooperate with departments in different locations.
Experiment trackers can enable and facilitate efficient collaboration and communication. Consider the following questions to understand your team’s requirements:
- Shared project workspaces: Do several people contribute to the same experiment? Would you like to add comments to artifacts or use an internal chat functionality?
- User and access management: Should everyone see all projects or experiments? Should everyone be allowed to add runs, modify dashboards, or create analyses? How often do you need to onboard and offboard team members or collaborators?
- Sharing results across teams: Do you need to set up teams and assign them read access to certain projects of other teams? Would you like to have shared dashboards or experiment catalogs?
- Sharing results with third parties: Can you easily generate links and configure access to share individual run analyses or dashboards with people outside your organization?
ML Collaboration: Best Practices From 4 ML TeamsÂ
An experiment tracker has to meet requirements imposed by the business
So far, we have discussed possible features an experiment tracking tool or platform should offer from the perspective of ML practitioners and their managers.
However, there are also more general questions you need to consider before looking for a software platform or tool:
- Open-source or proprietary software: Many of today’s machine-learning libraries and MLOps tools are open-source, and many data scientists freely share their knowledge. Naturally, this leads teams to gravitate to open-source tools.
At the same time, many of us have grown accustomed to working on cloud platforms that primarily consist of proprietary services – and offer SLAs and support. (We’ll return to this and other potential benefits below.)
When it comes to ML experiment trackers, the main advantage of open-source software is the ability to audit and adapt the code to your needs. When looking for an experiment tracking tool, consider how likely you’ll want to change a software’s code rather than use a plugin interface that many proprietary tools provide. - Self-hosting or managed platform: Independent of their license and source availability, many experiment trackers are available for self-hosting as well as managed platforms.
Self-hosting typically gives you complete control, and you can keep your data and models within your organization’s boundaries. A managed platform, however, gives you peace of mind regarding setup, maintenance, and updates. Typically, operational costs are much more transparent and predictable. Some vendors offer hybrid models where they host their software within your cloud or on-premises infrastructure.
Many data science teams underestimate the ongoing maintenance effort and overestimate their DevOps skills. So ask yourself: How much time and money can we spend? Could we task a dedicated infrastructure team to host the experiment tracker? - Security and compliance requirements: Depending on the type of organization and the field you work in, security and compliance are first-of-mind. Your IT department and management will likely be more interested in these details than a tool’s experiment tracking capabilities.
Before starting your market research, you should get answers to questions like the following: Do you need role-based access control (RBAC for short)? Does your data need to be encrypted? Do you need to keep an access log? Does your experiment tracker have to integrate with your single sign-on (SSO) solution? - Costs: Compared to training deep learning models on GPU clusters, hosting an experiment tracker is undoubtedly on the cheap side. Nevertheless, costs between different options can differ widely and are often difficult to estimate for self-hosted solutions because cost items are not necessarily obvious. For example, remember to factor in personnel expenses for setup, ongoing administration, and maintenance.
- Pricing: Does a managed product charge per seat, per model, per experiment, per project, or a flat license fee? What does that mean for your team and its growth trajectory? It’s not uncommon that a pricing scheme is attractive for small teams but leads to prohibitively high costs once new members join.
- Support: As with any new software, you’ll likely get stuck somewhere or wonder whether there are best practices for your usage scenario. When evaluating ML experiment trackers, ask yourself: Is the documentation detailed and up-to-date? Does it include how-tos for your team’s typical workflow steps? Is there a dedicated community support forum, like a Discord or Slack channel, or will you have to rely on StackOverflow and blog posts by fellow ML engineers? Do you have the means to pay for commercial support? Would you like help with the initial setup and have someone come in for introductory workshops?
Comparison of ML experiment tracking tools
Before we dig into each tool, here’s a high-level comparison of features and integrations of the 13 best experiment tracking and management tools:
Free
Free or paid, depending on the plan
Free
* DVC: free
* DVC Studio: free or paid, depending on the plan
Free
Free
Note: This table was last updated in March 2024. Some information may be outdated today. See some incorrect info? Let us know, and we’ll update it.
neptune.ai
neptune.ai is an experiment tracker designed with a strong focus on collaboration and scalability. It lets you monitor months-long model training, track massive amounts of data, and compare thousands of metrics in the blink of an eye. The tool is known for its user-friendly interface and flexibility, enabling teams to adopt it into their existing workflows with minimal disruption.
With collaboration at its core, Neptune allows users to create projects within the app, work on them together, and generate reports to share the results or project milestones with each other or with external stakeholders. The platform provides fine-grained user management and a highly customizable user interface with advanced visualization features.
Advantages
- Neptune can log a wide range of experiment metadata, including source code, Jupyter notebook snapshots, and Git information. Users can flexibly adapt the metadata structure to their needs.
- Neptune easily tracks tens of thousands of data points, the UI allows users to compare more than 100,000 runs with millions of data points.
- The forking feature allows you to resume a run from a saved checkpoint and create new runs from any saved step. Forget about waiting for one experiment to finish and only then starting a new one with different parameters—with Neptune you can branch off from the current experiment with different training parameters while continuing the original one.
- Neptune’s UI is versatile and customizable while easy to navigate for non-technical collaborators.
Limitations
- Neptune is focused on experiment tracking and model management. Users are expected to stand up and manage their own infrastructure.
- The free tier of Neptune’s SaaS offering is limited to a single project and up to 3 users.
See how ML teams solve their experiment tracking problems with Neptune
Weights & Biases
Weights & Biases (also known as WandB and W&B) is a platform for experiment tracking, dataset versioning, and model management. WandB’s components, including a model registry and artifact store, allow you to store and manage the model lifecycle, version datasets, and models.
The experiment tracking feature is a central component of WandB. It supports the leading machine-learning and deep-learning frameworks, such as TensorFlow, PyTorch, Keras, and scikit-learn, out of the box but can log arbitrary metrics as well. WandB also supports CPU and GPU usage tracking.
The platform is available as a managed platform and as an on-premise tool in different configurations.
Advantages
- Weights & Biases provides a unified platform for the entire ML development process, including dataset and model management.
- WandB allows users to create interactive dashboards and reports collaboratively.
- Built-in support for hyperparameter search and model optimization with WandB Sweeps.
Limitations
- The “Teams” plan is limited to ten team members accessing the platform and dashboards. Beyond that, you’ll need to negotiate an “Enterprise” contract.
- User management and administration can be cumbersome.
Weights & Biases vs Neptune
Comet ML
Comet is a cloud-based MLOps platform that helps data scientists track experiments, manage the model lifecycle, and collaborate on ML projects. It also provides interactive dashboards for analyzing experiment metadata and comparing experiments. Comet’s UI presents code, graphics, audio, and text data, as well as common analysis tools like confusion matrices and histograms.
In addition to experiment tracking capabilities, Comet ML includes a model registry and production monitoring features. The SaaS offering is free for personal use and academics, and on-premise deployment is available for enterprise customers.
Advantages
- Users can manage the entire model lifecycle through a single UI that provides customizable and versatile visualization features.
- The tracking client captures a wide range of information about the experiment run’s environment.
- Includes a dedicated component for working with LLMs.
Limitations
- Team collaboration is only available in the paid plans.
- Comet’s UI tends to become slow for large numbers of experiments.
- Due to its deep integration with the platform, Comet ML’s experiment tracking functionality is difficult to use standalone.
Comet vs Neptune
Aim
Aim is an open-source experiment tracker created by AimStack. It was started in 2019 and offers extensive dashboards and plots, as well as comparing multiple runs.
The Aim experiment tracker can be run locally or hosted on Kubernetes for multiple users. The platform consists of two components: a UI and a tracking server. Its integration with MLflow allows you to use Aim’s versatile UI to explore MLflow experiment data. AimStack, the company behind Aim, offers enterprise support for Aim with multi-user deployments and custom plugins.
In October 2023, AimStack released AimOS as the successor. It remains to be seen whether Aim will be actively developed further. Initially, they released AimOS as a direct replacement but later said they would continue the development of Aim.
Advantages
- You can run it directly from Jupyter notebooks.
- Integration for spaCy, the popular open-source NLP framework, and support for most deep learning frameworks.
- Aim has a beautiful UI that can also be used with MLflow’s tracking server as the backend.
Limitations
- Unclear future of the original Aim after the company behind it announced AimOS as the successor.
- Aim does not support scikit-learn, a widely used ML framework.
- No managed offer is available, and self-hosting requires significant effort.
DagsHub
DagsHub is a fully managed end-to-end AI platform built primarily on proven open-source tools. In that spirit, DagsHub relies on MLflow’s backend for experiment tracking but provides a custom UI. Paired with DVC and Git integrations, this enables fully reproducible ML experiments.
DagsHub is available as SaaS. The free “Community” tier allows for up to two collaborators in private projects and unlimited experiment tracking. In the “Enterprise” tier, getting an isolated or on-prem installation of DagsHub is possible.
Advantages
- DagsHub is built around collaboration and allows users to comment on everything, including experiment results and dashboards.
- If training data turns out to be a bottleneck, the integration with Label Studio and the Data Engine active learning utility allows users to annotate additional samples quickly.
Limitations
- Teams have to adopt the entire platform to benefit from DagsHub over using MLflow as a standalone tool.
- Advanced authentication capabilities are only available in the “Enterprise” tier.
ClearML Experiment
ClearML (formerly known as Allegro Trains) is an end-to-end machine-learning platform with experiment-tracking capabilities. It is available as an open-source solution that can be self-hosted in the cloud or on Kubernetes and as a managed offering.
ClearML Experiment goes beyond tracking model metrics. It can also log code, notebooks, configuration files, and containers. It features a user-friendly web UI for tracking and visualizing experiments. Users can share results with others through ClearML Reports.
One strength is the tight integration with the other ClearML platform components for data management, model lifecycle management, and model serving capabilities. ClearML is framework-agnostic and can be extended.
Advantages
- Integrated with a complete end-to-end ML platform that provides a unified user experience to data scientists, ML engineers, and DevOps specialists.
- ClearML Experiment provides an offline mode in which all information is saved in a local folder to be synced when an internet connection becomes available again.
- The ClearML Enterprise managed platform provides enterprise-grade security and governance.
Limitations
- ClearML’s UI offers few customization options, limiting the ways in which experiment metadata can be visualized.
- Due to its deep integration with the rest of the ClearML platform, ClearML Experiment is difficult to use as a standalone experiment tracker.
ClearML vs Neptune
MLflow
MLflow is an open-source platform that helps manage the whole machine learning lifecycle. This includes experimentation but also model storage, reproducibility, and deployment. Each of these four elements is represented by one MLflow component: Tracking, Model Registry, Projects, and Models.
The MLflow Tracking component consists of an API and UI that support logging various metadata (including parameters, code versions, metrics, and output files) and later visualizing the results. Recently, the MLflow developers have added a dedicated LLM experiment tracker and experimental support for prompt engineering.
Beyond self-hosted deployments, MLflow can also be used within cloud platforms like Amazon SageMaker and Azure Machine Learning. In both cases, MLflow is used only as a client, meaning that users log experiments via the MLflow API, but the backend storage and UI are handled by a proprietary, managed solution hosted by the respective cloud provider.
- In Amazon SageMaker, MLflow replaced the earlier SageMaker Experiments module in June 2024. SageMaker now offers a fully managed MLflow tracking server in three sizes, supporting up to 100 transactions per second (200 in burst mode). However, the backend is not the open-source MLflow platform, and some advanced MLflow features such as custom run queries are not available.
- Similarly, Azure Machine Learning supports experiment tracking only via the MLflow client, which sends tracking data to a proprietary Azure backend.The tracking experience mirrors what the MLflow client supports but lacks many advanced MLflow features that are not supported and others that are being deprecated in favor of Microsoft’s in-house solutions. As a result, Azure ML’s experiment tracking remains relatively basic compared to other dedicated tracking tools.
These integrations allow teams to use MLflow syntax within managed cloud environments, but it’s worth noting that neither SageMaker nor AzureML provide a full-featured MLflow experience out of the box.
In general, MLflow excels at streamlining ML lifecycle management and simplifying experiment tracking. However, it lacks many features that data science teams seek, such as dataset versioning or user access management. Further, you need to deploy MLflow on your own infrastructure and rely on its community for support. The managed MLflow by Databricks comes with user and access management.
Advantages
- Focus on the whole lifecycle of the machine-learning process.
- A strong and big community of users that provide community support.
- Open interface that can be integrated with any ML library or language.
- Compatible with managed platforms like Amazon SageMaker and Azure ML via the MLflow client.
Limitations
- Has to be self-hosted (although a managed offer by Databricks exists), which involves a significant overhead.
- Security and compliance measures have to be implemented by users unless using a managed version.
- Lack of user and group management and collaborative features in the open-source version.
- When used with SageMaker or Azure ML, some core MLflow features are limited or unsupported in favor of other in-house solutions.
MLflow vs Neptune
DVC Experiments and DVC Studio
DVC Experiments is the experiment-tracking component of the open-source Data Version Control (DVC) family of tools. Originally, DVC was an open-source version control system created specifically for machine learning projects built on top of Git. Accordingly, the main focus of DVC Experiments is on reproducibility through code and data versioning. It also includes features to run experiments.
DVC Studio is the web interface of the DVC tools. It is available as a managed service and for self-hosting on AWS or Kubernetes. DVC enables data science teams to collaborate on dataset curation, model management, and ML experiments. It integrates a versatile plotting functionality and supports live visualization of metrics.
Advantages
- Aside from the DVC Studio web UI, you can use DVC Experiments through the DVC CLI and a VS Code extension
- The Git-based approach of DVC is robust and well-suited for teams with a strong software engineering background.
- DVC Studio provides fine-grained team and permissions management.
Limitations
- For users not familiar with Git or version control, navigating experiments and tracked metadata – which are organized through Git branches – can be a challenge.
- Compared to dedicated experiment trackers, the visualization and experiment comparison features are limited.
Sacred and Omniboard
Sacred is an open-source software developed at the Swiss AI Lab IDSIA that allows machine learning researchers to configure, organize, log, and reproduce experiments. Sacred is a highly flexible experiment-tracking library that integrates with various storage backends.
Omniboard is a dedicated web UI for Sacred, which itself does not come with a UI built-in. It allows users to list and compare experiments, plot metrics, and access logged artifacts and source files.
Advantages
- Flexible metadata structure that can be adapted to a wide range of models and data types.
- Sacred provides a powerful command line interface and a versatile Python client.
- Sacred can be connected to different UIs, such as Omniboard. You can also directly query the MongoDB database it uses to store metadata.
Limitations
- Since it does not provide a UI, Sacred requires users to be comfortable with the command line and have an advanced knowledge of Python.
- As a research-focused tool, Sacred does not provide capabilities for model lifecycle management, such as a model registry.
Case Study: How Brainly Avoids Workflow Bottlenecks With Automated Tracking
Brainly is the leading learning platform worldwide, with the most extensive Knowledge Base for all school subjects and grades. Each month, over 350 million students, parents, and educators rely on Brainly as the proven platform to accelerate understanding and learning.
One of their core features and key entry points is Snap to Solve. It’s a machine learning-powered feature that lets users take and upload a photo. Snap to Solve then detects the question or problem in that photo and provides solutions.
The team uses Amazon SageMaker to run its computing workloads and serve its models. When the number of training runs on the team’s large compute architectures increased, they realized that their logs from Amazon SageMaker needed to be trackable and manageable, or they would cause bottlenecks in their workflow.
Read how the Brainly team adopted Neptune as their experiment tracker.
Google Vertex AI
Vertex AI is the fully managed machine learning solution of Google’s Cloud Platform. It provides a unified platform for the entire ML lifecycle, from data preparation and model training to model deployment, model experiment tracking, and monitoring.
Google has firmly established itself as a leading machine learning and AI player, with many advances coming from the tech giant’s research branch and infrastructure teams. Much of this experience has found its way into Vertex. The platform is especially interesting for teams working on Google Cloud or looking to leverage leading data management solutions like BigQuery.
Vertex AI includes the Vertex ML Metadata experiment tracker. This component is based on the ML Metadata (MLMD) library maintained by the TensorFlow Serving team but can be used to track training metrics and artifact lineage for any machine-learning framework. Vertex AI also integrates the open-source TensorBoard metadata visualization tool.
Advantages
- Vertex ML Metadata is fully integrated with other Vertex AI components and Google Cloud Platform services.
- Fine-grained access control through Google Cloud Platform’s Identity and Access Management capabilities.
Limitations
- Choosing Vertex AI as your MLOps stack locks you to the Google Cloud Platform long-term. Migrating to a different cloud provider or a selection of SaaS and self-hosted tools will likely require re-engineering large parts of your infrastructure.
- The Python SDK and REST API of Vertex ML Metadata are relatively low-level, which gives users a lot of flexibility but requires them to become familiar with the metadata schemas or develop their own.
The Best Vertex ML Metadata AlternativesÂ
TensorBoard
TensorBoard is the visualization tool integrated with TensorFlow, so it’s often the first choice of TensorFlow users. TensorBoard offers a suite of features for visualizing and debugging machine learning models. Users can track model metrics like loss and accuracy, visualize the model graph and changes to weights and biases, and project embeddings to a lower-dimensional space. TensorBoard also includes a profiler.
Like its parent framework, TensorBoard is open source. It runs locally and can be integrated with Jupyter and Colab notebooks.
Advantages
- Well-developed features for working with images, text, and embeddings
- Tight and deep integration with the latest versions of TensorFlow.
- TensorBoard runs in any Python environment with TensorFlow installed. It does not require the setup of a database or additional libraries.
Limitations
- TensorBoard has to be self-hosted. (A managed version previously available at tensorboard.dev was discontinued at the end of 2023).
- No collaboration features, access control, user management, or centralized data store.
- Unsuitable for frameworks other than TensorFlow.
TensorBoard vs Neptune
Conclusion
Tracking machine learning experiments has always been an important element of the ML development process. However, earlier, the process was very manual, time-consuming, and error-prone.
Over the last few years, the market of modern experiment tracking and experiment management tools for machine learning has grown and matured. The range of available options is broad and diversified now. No matter if you’re looking for an open-source or enterprise solution or if you prefer a standalone experiment tracking framework or an end-to-end platform, you’ll certainly find the right tool.
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