We earn commission when you buy through affiliate links.
This does not influence our reviews or recommendations.Learn more.
Building AI models in production is not a once-off process.
In this iterative process, documenting information about datasets, models, and hyperparameters for future reference is important.
That is where metadata comes in.
What is Metadata in ML?
Simply put, metadata is data about data.
This includes data about artifacts, models, and datasets involved at each stage.
This article will review some of the best AI metadata-tracking platforms for your ML applications.
AimStack
AimStackis an easy-to-use and open-source tracker for your ML metadata.
Because it is open-source, you’ve got the option to self-host your AIM.
In addition, it provides a UI that makes it easy to visualize your metadata.
it’s possible for you to also make programmatic queries using the SDK.
It integrates well with popular ML tools such asPyTorch, TensorFlow, and MLflow.
Neptune
Neptuneprovides a single platform to use to manage your metadata.
The platform has plans ranging from free individual to paid team and enterprise plans.
With Neptune, you could log metadata and view it in an interactive online dashboard.
This allows you to track and monitor experiments.
Neptune integrates with popular ML tools such as Hugging Face, Sci-Kit Learn, and Keras.
As a platform, Domino is made up of several components.
The major component used in metadata management is the system of record component.
you’re free to also log metrics, artifacts, and any other information.
Viso
Visois an all-in-one, no-code platform for building computer vision applications.
With Viso, you’re free to automate manual work and build scalable models.
It includes features you will need in the development lifecycle of your machine learning applications.
These include tools for data collection, annotating data, training, developing, and deploying, among others.
Using the Viso deployment manager, you’re able to monitor your models to identify issues.
Studio by Iterative AI
Studiois a platform for data and model management created by Iterative AI.
It offers different plans, including a free plan for individuals.
Studio has a model registry for keeping track of your machine-learning models using Git repositories.
The platform also includes tracking for experiments, visualization, and collaboration.
It also helps you automate your machine-learning workflows and build using a no-code UI.
It integrates with your popular Git providers, such as GitLab, GitHub, and BitBucket.
Seldon
Seldonsimplifies serving and managing machine learning models at scale.
It works well with tools such as Tensorflow, SciKit-Learn, and Hugging Face.
Among other ways, Seldon helps you improve efficiency by monitoring and managing your models.
This enables companies to build a knowledge base for their machine-learning operations.
It integrates with tools such as Snowflake, BigQuery, and RedShift.
It is mainly meant for enterprise users.
Usage options include using it as a SaaS or on your cloud account or physical infrastructure.
It functions as a central hub for monitoring model health.
It also monitors your model schema and features and compares changes across different versions.
Arize makes it easy to perform A/B comparisons after tests.
you’re able to query metrics using an SQL-like language.
you’re able to also access it via the GraphQL programmatic API.
We also covered the most common and best tools for managing metadata produced in your Machine Learning workflows.
Next, check outAI platforms to build your modern software.
