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. Youâll learn their main features and see how they differ. Before diving into the individual tools, weâll 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 experimentâs 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 â just as the needs of machine-learning teams looking to adopt one of them. Thus, before we turn our attention to specific tools, weâll 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, âWhat is the best experiment-tracking tool?â
If youâre a solo data scientist without a budget, an open-source solution you can host on your laptop might be a good fit. If youâre an ML researcher exploring novel models and algorithms, youâll require the flexibility to integrate with exotic frameworks and track custom metrics. And if youâre managing a department at a Fortune 500, cross-team collaboration and compliance will be first of mind.
Therefore, before you look into individual tools, itâs paramount that you understand your essential requirements.
An experiment tracker has to fit your teamâs 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, itâs clear that the teamsâ 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 youâre 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 youâre 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, thatâs 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 HuggingFaceâs transformers, include third-party integrations themselves.
Aside from the framework, you should also determine what types of models youâll 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 teamâs 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 todayâs 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 â and offer SLAs and support. (Weâll 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 youâll want to change a softwareâs 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 organizationâs 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 toolâs 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? Itâs 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, youâll 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 teamâs 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, hereâs 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 weâll update it.
neptune.ai
neptune.ai is an experiment tracker offering model versioning and real-time model performance monitoring. Neptune is available in cloud and on-premise versions and integrates with over 25 tools and libraries, including various model training and hyperparameter optimization tools.
With a strong focus on collaboration, Neptune allows users to create projects within the app, work on them together, and share UI links 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 is highly scalable and easily handles hundreds of thousands of experiments.
- Neptuneâs 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 MLOps infrastructure.
- The free tier of Neptuneâs SaaS offering is limited to a single active project.
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. WandBâs 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 âTeamsâ plan is limited to ten team members accessing the platform and dashboards. Beyond that, youâll need to negotiate an âEnterpriseâ 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. Cometâs 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 runâs environment.
- Includes a dedicated component for working with LLMs.
Limitations
- Team collaboration is only available in the paid plans.
- Cometâs UI tends to become slow for large numbers of experiments.
- Due to its deep integration with the platform, Comet MLâs 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 Aimâs 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 MLflowâs 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.
Aim vs Neptune
DagsHub
DagsHub is a fully managed end-to-end AI platform built primarily on proven open-source tools. In that spirit, DagsHub relies on MLflowâs 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 âCommunityâ tier allows for up to two collaborators in private projects and unlimited experiment tracking. In the âEnterpriseâ 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 âEnterpriseâ tier.
DagsHub vs Neptune
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
- ClearMLâs 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.
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.
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.
- Lack of user and group management and collaborative features.
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 â which are organized through Git branches â can be a challenge.
- Compared to dedicated experiment trackers, the visualization and experiment comparison features are limited.
DVC vs Neptune
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 or Neptune. 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.
Sacred vs Neptune
Azure Machine Learning
Azure Machine Learning is Microsoftâs cloud-based MLOps platform. It lets you manage and automate the whole ML lifecycle, including model management, deployment, and monitoring. It is tightly integrated with the Azure platform, offering seamless integration with other Azure services that many organizations already use.
Azure Machine Learning provides a built-in machine-learning studio that allows users to organize, manage, and analyze experiments. They can visualize and compare different experiment runs and monitor resource utilization.
Advantages
- The ML experiment tracking API is fully compatible with MLflow, allowing you to migrate seamlessly between the two platforms.
- Azure Machine Learning is integrated with Azureâs other offerings, such as the Azure OpenAI Service, which provides access to models like GPT-4 and DALL-E.
- Through integration with Microsoft Entra and Azureâs RBAC model, Azure Machine Learning provides enterprise-grade security and access management.
Limitations
- Adopting Azure Machine Learning as your MLOps solution locks you into the Azure ecosystem. While the MLflow compatibility of the experiment tracking solution makes migration to MLflow easy, moving data and your training scripts will require significantly more work.
- Azure Machine Learning cannot be self-hosted but is only available as a SaaS solution.
- Due to its tight integration with the Azure platform, the Azure Machine Learning tracking feature is challenging to use as a stand-alone tool.
Azure ML vs Neptune
Amazon SageMaker
Amazon SageMaker is the end-to-end machine learning solution on the AWS cloud platform. It allows data scientists and ML engineers to prepare, build, train, and deploy machine learning models. Amazon SageMaker is one of the oldest offerings on the market and is particularly interesting for those already invested in the AWS ecosystem.
Well-integrated with Amazon’s suite of cloud services and SageMakerâs AutoML and hyperparameter optimization features, SageMakerâs Experiments component offers capabilities such as logging machine learning experiments, tracking model performance, and storing relevant metadata and artifacts.
In addition, Amazon SageMaker includes SageMaker Studio, a unified development environment for ML solutions. It comprises components such as a feature store, notebooks, and parameter tuning, all integrated into a user-friendly web interface. (Note that SageMaker studio was revamped at the end of 2023. The previous version is now referred to as âSageMaker Studio Classic.â)
Advantages
- Amazon SageMaker Experiments is fully integrated with other SageMaker components as well as the wider AWS ecosystem.
- The experiment-tracking SDK is integrated with the SageMaker Python SDK, allowing users to instrument their code without installing additional dependencies.
Limitations
- Compared to dedicated experiment trackers, the experiment tracking capabilities of Amazon SageMaker are quite basic.
- Opting for Amazon SageMaker as your machine learning platform binds you to AWS permanently. Migrating from Amazonâs cloud platform to a different MLOps stack will most likely involve significant work.
- Amazon SageMaker cannot be self-hosted. All your data, models, and metadata will be stored within AWS.
Amazon SageMaker vs Neptune
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. Itâs 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 teamâs 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 and explore Neptuneâs SageMaker integration.
Google Vertex AI
Vertex AI is the fully managed machine learning solution of Googleâs 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 giantâs 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 Platformâs 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 itâs 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 youâre looking for an open-source or enterprise solution or if you prefer a standalone experiment tracking framework or an end-to-end platform, youâll certainly find the right tool.
FAQ
-
Machine learning experiment tracking tools help data scientists and machine-learning practitioners manage and keep a record of their machine-learning experiments. ML experiment trackers are crucial to ensure the reproducibility of training, gaining insights into model performance, and fostering collaboration.
An ML experiment tracking tool captures and organizes metadata associated with each experiment, including model hyperparameters, any code that was used, dataset versions, and evaluation metrics. This allows users to trace the evolution of models, compare different experiments, and identify the conditions that led to the best-performing models.
-
Experiment trackers contribute to the efficiency and transparency of the ML development process by providing a systematic way to log and analyze experiment-related information.
The main reasons for using an ML experiment tracker are:
- All of your ML experiments and models are organized in a single place. This allows you to search and filter experiments quickly, irrespective of where you ran them.
- Compare ML experiments, analyze results, and debug model training. An experiment tracker helps you consistently log all relevant information in a well-defined format.
- Collaborate on ML experiments and share results with stakeholders. Experiment trackers allow teams to work together and create dashboards and reports.
- Monitor experiments and model training progress. This is particularly important when training jobs run on remote servers.
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What experiment tracker works best for your situation and your team depends on a number of factors:
- It has to fit your teamâs workflow and preferred way of working. For example, some teams love CLI-based tools, while others prefer to work with a rich UI.
- It has to be compatible with the machine-learning frameworks youâre using and the machine-learning tasks youâre solving. Some experiment trackers might offer excellent support for time-series forecasting but lack the features necessary for NLP tasks.
- The experiment tracker has to meet your collaboration needs. In particular, if youâre working remotely or with outside collaborators, integrated communication capabilities and the possibility of sharing links to dashboards come in handy.
- The experiment tracker must meet your business requirements. Typically, this is not about costs (experiment trackers tend to be relatively cheap compared to the computational resources ML model training requires) but compliance and (data) security.
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A typical experiment tracking setup consists of two components: A tracking server that stores the experiment metadata and provides a UI to analyze it, and a client library that you integrate in your training script.
Throughout a model training run, the experiment tracking client sends metadata such as intermediate metrics, sample predictions, and model artifacts to the tracking server. The server receives this information and writes it to a database or artifact store. Later, you can access this data through a web interface, CLI, or programmatically through a script.
More about 13 Best Tools for ML Experiment Tracking and Management in 2024
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