GitHub is a platform that hosts public and private code and provides software development and collaboration tools. Features include version control, issue tracking, code review, team management, syntax highlighting, etc. Personal plans ($0-50), Organizational plans ($0-200), and Enterprise plans are available.
$4
per month per user
H2O.ai
Score6.4 out of 10
N/A
An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.
GitHub is an easy to go tool when it comes to Version Controlling, CI/CD workflows, Integration with third party softwares. It's effective for any level of CI/CD implementation you would like to. Also the the cost of product is also very competitive and affordable. As of now GitHub lacks capabilities when it comes to detailed project management in comparison to tools like Jira, but overall its value for money.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Version control: GitHub provides a powerful and flexible Git-based version control system that allows teams to track changes to their code over time, collaborate on code with others, and maintain a history of their work.
Code review: GitHub's pull request system enables teams to review code changes, discuss suggestions and merge changes in a central location. This makes it easier to catch bugs and ensure that code quality remains high.
Collaboration: GitHub provides a variety of collaboration tools to help teams work together effectively, including issue tracking, project management, and wikis.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Not an easy tool for beginners. Prior command-line experience is expected to get started with GitHub efficiently.
Unlike other source control platforms GitHub is a little confusing. With no proper GUI tool its hard to understand the source code version/history.
Working with larger files can be tricky. For file sizes above 100MB, GitHub expects the developer to use different commands (lfs).
While using the web version of GitHub, it has some restrictions on the number of files that can be uploaded at once. Recommended action is to use the command-line utility to add and push files into the repository.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
GitHub's ease of use and continued investment into the Developer Experience have made it the de facto tool for our engineers to manage software changes. With new features that continue to come out, we have been able to consolidate several other SaaS solutions and reduce the number of tools required for each engineer to perform their job responsibilities.
GitHub is a clean and modern interface. The underlying integrations make it smooth to couple tasks, projects, pull requests and other business functions together. The insights and reporting is really strong and is getting better with every release. GitHub's PR tooling is strong for being web based, i do believe a better code editor would rival having to pull merge conflicts into local IDE.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
There are a ton of resources and tutorials for GitHub online. The sheer number of people who use GitHub ensures that someone has the exact answer you are looking for. The docs on GitHub itself are very thorough as well. You will often find an official doc along with the hundreds of independent tutorials that answers your question, which is unusual for most online services.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
While I don't have very much experience with these 2 solutions, they're two of the most popular alternatives to GitHub. Bitbucket is from Atlassian, which may make sense for a team that is already using other Atlassian tools like Jira, Confluence, and Trello, as their integration will likely be much tighter. Gitlab on the other hand has a reputation as a very capable GitHub replacement with some features that are not available on GitHub like firewall tools.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Team collaboration significantly improved as everything is clearly logged and maintained.
Maintaining a good overview of items will be delivered wrt the roadmap for example.
Knowledge management and tracking. Over time a lot of tickets, issues and comments are logged. GitHub is a great asset to go back and review why x was y.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
