ModelDB is an open-source system to version machine learning models including their ingredients: code, data, config, and environment and to track ML metadata across the model lifecycle. It’s a great tool – no questions asked.
But! It’s not the only tool. There are other options that can help you keep track of all your data, and manage ML models.
Some tools can help you do more than that – make your ML models reproducible, manage and track experiments, their performance, dependencies, collaborate with other team members, and manage the entire lifecycle of your models from development to live monitoring.
With the right solution, you can effectively automate versioning control, create a unified control hub for all the people you collaborate with, and have a repository of all your data so you don’t have to focus on repetitive and organizational tasks.
To help you better manage your ML models, here’s a list of the 10 best alternatives to ModelDB. So let’s dive in!
Neptune is the most lightweight experiment management tool available on the market. It’s an excellent tracking platform for any data scientist. The software easily integrates with your workflow and offers an extensive range of tracking and management features.
You can use it to track, retrieve, and analyze experiments but also to share them with your team and managers. Additionally, Neptune is very flexible, works with many other frameworks, and thanks to its stable user interface, it enables great scalability.
It’s a robust tool that effectively automates tedious processes and helps you fully manage all your models.
Why is Neptune a better alternative to ModelDB:
- Provides user and organization management with different organization, projects, and user roles for more clarity.
- Fast and beautiful UI with a lot of capabilities to organize runs in groups, save custom dashboard views, and share them with the team.
- You can use a hosted app to avoid all the hassle with maintaining yet another tool (or have it deployed on your on-prem infrastructure).
- Your team can track experiments that are executed in scripts (Python, R, other), notebooks (local, Google Colab, AWS SageMaker) and do that on any infrastructure (cloud, laptop, cluster).
- Extensive experiment tracking and visualization capabilities (resource consumption, scrolling through lists of images).
- View and manage models and experiments in real time.
Weights & Biases a.k.a. WandB is focused on deep learning. You can track experiments to the application with Python library, and with your team see each other experiments.
WandB is a hosted service allowing you to backup all experiments in a single place and work on a project with the team. In the WandB you can log and analyze multiple data types.
Weights & Biases is oriented around 4 main tools:
- Dashboard: Track experiments, visualize results
- Reports: Save and share reproducible findings
- Sweeps: Optimize models with hyperparameter tuning
- Artifacts: Dataset and model versioning, pipeline tracking
👉 Check out the comparison between WandB & Neptune
MLflow is an open-source platform that helps manage the whole machine learning lifecycle that includes experimentation, reproducibility, deployment, and a central model registry.
MLflow is suitable for individuals and for teams of any size.
The tool is library-agnostic. You can use it with any machine learning library and in any programming language.
MLflow comprises four main functions that help to track and organize experiments:
- Tracking – an API and UI for logging parameters, code versions, metrics, and artifacts when running machine learning code and for later visualizing and comparing the results
- Projects – packaging ML code in a reusable, reproducible form to share with other data scientists or transfer to production
- Models – managing and deploying models from different ML libraries to a variety of model serving and inference platforms
- Model Registry – a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations
TensorBoard is a visualization toolkit for TensorFlow. It lets you analyze model training runs. It’s open-source and has functionalities helpful in the entire machine learning workflow.
Additionally, it has an extensive network of engineers using this software and sharing their experience and ideas. This makes a powerful community ready to solve any problem. The software, itself, however, is best suited for an individual user.
TensorBoard in a nutshell:
- Track and visualize metrics such as loss and accuracy
- Compare learning curves of various runs
- Parallel coordinates plot to visualize parameter-metric interactions
- It has other visualization features that are not parameter-metric related
- Project embeddings to a lower dimensional space
- Integrates with many other tools and applications
👉 Check the comparison between TensorBoard & Neptune
SageMaker Studio is an Amazon tool that allows data scientists to manage an entire machine learning lifecycle. From building and training to deploying ML models. The idea behind this software is to make it easier and less time-consuming to develop high-quality experiments. It’s a web-based tool and comes with the whole toolset designed to help data scientists improve their performance.
Here are some of the main advantages of SageMaker:
- Possibility to track thousands of experiments
- Integrates with a wide range of Amazon tools for ML related tasks
- Fully managed
- Fully elastic compute resources
👉 Check the comparison between SageMaker & Neptune
Kubeflow is the ML toolkit for Kubernetes. It helps in maintaining machine learning systems by packaging and managing docker containers. It facilitates the scaling of machine learning models by making run orchestration and deployments of machine learning workflows easier.
It’s an open-source project that contains a curated set of compatible tools and frameworks specific for various ML tasks.
Here’s a short Kubeflow summary:
- A user interface (UI) for managing and tracking experiments, jobs, and runs
- Notebooks for interacting with the system using the SDK
- Re-use components and pipelines to quickly create end-to-end solutions without having to rebuild each time
- Kubeflow Pipelines is available as a core component of Kubeflow or as a standalone installation
- Multi-framework integration
👉 Check the comparison between Kubeflow & Neptune
Sacred is open-source software and allows machine learning engineers to configure, organize, log, and reproduce experiments. Sacred doesn’t come with its proper UI but there are a few dashboarding tools that you can connect to it, such as Omniboard, Sacredboard, or Neptune.
Also, it doesn’t have large scalability of other tools and has not been adapted to team collaboration, however, it has great potential when it comes to individual research.
Here’s what Sacred is built of and what you can do with it:
- A very convenient way of the local variables in a function to define the parameters your experiment uses
- You can access all parameters of your configuration from every function. They are automatically injected by name
- You get a powerful command-line interface for each experiment that you can use to change parameters and run different variants.
- Observers – log all kinds of information about your experiment, its dependencies, the configuration you used, the machine it is run on, and of course the result. These can be saved to a MongoDB, for easy access later.
- Automatic seeding helps to control the randomness in your experiments, such that the results remain reproducible.
👉 Check the comparison between Sacred & Neptune
Comet is a meta machine learning platform for tracking, comparing, explaining, and optimizing experiments and models. It allows you to view and compare all of your experiments in one place. It works wherever you run your code with any machine learning library, and for any machine learning task.
Comet is suitable for teams, individuals, academics, organizations, and anyone who wants to easily visualize experiments and facilitate work and run experiments.
Some of the Comet most notable features include:
- Sharing work in a team: multiple features for sharing in a team
- Works well with existing ML libraries
- Deals with user management
- Let’s you compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more
- Allows you to visualize samples with dedicated modules for vision, audio, text and tabular data
- Has a bunch of Integrations to connect it to other tools easily
👉 Check the comparison between Comet & Neptune
9. Guild AI
Guild AI is a tool for running, tracking, and comparing experiments. It’s cross-platform and framework independent — you can train and capture experiments in any language using any library.
Guild AI runs your unmodified code so you get to use the libraries you want. The tool doesn’t require databases or other infrastructure to manage experiments — it’s simple and easy to use.
- Track experiment of any model training and any programming language
- Has automated machine learning process
- Integrated with any language and library
- Remote training and backup possibility
- Reproduce results or recreate experiments
👉 Check the comparison between Guild AI & Neptune
Pachyderm is a platform that combines data lineage with end-to-end pipelines on Kubernetes.
It’s available in three versions, Community Edition (open-source, with the ability to be used anywhere), Enterprise Edition (complete version-controlled platform), and Hub Edition (still a beta version, it combines characteristics of the two previous versions).
You need to integrate Pachyderm with your infrastructure/private cloud.
Here are some pros of using Pachyderm:
- Possibility to adapt the software version to your own needs
- End-to-end process support
- Established and backed by a strong community of experts
👉 Check the comparison between Pachyderm & Neptune
Wrapping it up
Tools can help you automate and optimize work, and find the best solution to your ML problems. So use harness the power they offer and implement them wisely to create your perfect ML models!
And don’t forget to choose those best suited to your needs and preferences.
Get started with Neptune in 5 minutes
If you are looking for an experiment tracking tool you may want to take a look at Neptune.
It takes literally 5 minutes to set up and as one of our happy users said:
“Within the first few tens of runs, I realized how complete the tracking was – not just one or two numbers, but also the exact state of the code, the best-quality model snapshot stored to the cloud, the ability to quickly add notes on a particular experiment. My old methods were such a mess by comparison.” – Edward Dixon, Data Scientist @intel
To get started follow these 4 simple steps.
Install the client library.
pip install neptune-client
Connect to the tool by adding a snippet to your training code.
import neptune neptune.init(...) # credentials neptune.create_experiment() # start logger
Specify what you want to log:
neptune.log_metric('accuracy', 0.92) for prediction_image in worst_predictions: neptune.log_image('worst predictions', prediction_image)
Run your experiment as you normally would:
And that’s it!
Your experiment is logged to a central experiment database and displayed in the experiment dashboard, where you can search, compare, and drill down to whatever information you need.