BitBucket
GitHub
GitLab
Gitea
SourceForge
Assembla
Phabricator
Launchpad.net
Algorithmia
Managed MLflow
MCenter
neptune.ai
5Analytics
Weights & Biases
Iterative.ai
Comet.ml
BitBucket AlgorithmiaBased on our record, BitBucket seems to be a lot more popular than Algorithmia. While we know about 77 links to BitBucket, we've tracked only 5 mentions of Algorithmia. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Facilitated Collaboration and Funding: With easier identification comes better connectivity. Contributors, partners, and funders can more readily find projects that resonate with their interests and values. Moreover, platforms such as GitHub, GitLab, and Bitbucket are increasingly interested in integrating standardized licensing solutions like License-Token, paving the way for broader adoption and collaborative... - Source: dev.to / 2 months ago
Git ensures that your code is safe. Even if your laptop crashes, your work is backed up on a remote repository (e.g., GitHub, GitLab, Bitbucket). - Source: dev.to / 6 months ago
GitHub, GitLab, Bitbucket: These platforms provide easy-to-use interfaces for Git, adding features like pull requests, issue tracking, and more. Explore GitHub, GitLab, and Bitbucket. - Source: dev.to / 8 months ago
Tools: Use platforms like Bitbucket or GitHub’s pull request feature. - Source: dev.to / 11 months ago
We must be careful that the value, worth, and success of open-source projects are not measured by vanity metrics such as stars on GitHub or attempts at gaming the GitHub trending algorithm. For one thing, not all open source worth investment happens on GitHub, there are also platforms such as GitLab, Codeberg, and BitBucket where a lot of great work is being done. Some people also overblow the success of a... - Source: dev.to / about 1 year ago
To push a model into production, there are additional concerns which the tools in the versioning, deployment and release space aim to solve. This includes obtaining adequate infrastructure to run the model reliably and facilitating easy model release or rollback. Solutions in the MLOps space includes Kubeflow, Pachyderm and Algorithmia. - Source: dev.to / over 3 years ago
And for enterprises that want to do the same with ML you can use algorithmia.com. Source: over 3 years ago
Algorithmia advertises themselves as an MLops platform for data scientists, and they provide an easy way to host models on a scalable REST API. Source: over 3 years ago
Seems similar to https://algorithmia.com. Source: over 3 years ago
Algorithmia.com — Host algorithms for free. Includes free monthly allowance for running algorithms. Now with CLI support. - Source: dev.to / almost 4 years ago
GitHub - Originally founded as a project to simplify sharing code, GitHub has grown into an application used by over a million people to store over two million code repositories, making GitHub the largest code host in the world.
Managed MLflow - Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.
GitLab - Create, review and deploy code together with GitLab open source git repo management software | GitLab
MCenter - Machine Learning Operationalization
Gitea - A painless self-hosted Git service
neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.