The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to drive business outcomes, that is available on the user's cloud platform-of-choice, on-premise, or as a fully-managed service. The solutions include tools providing data preparation enabling users to explore and…
$0
GitHub
Score9.2 out of 10
N/A
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.
DataRobot can be used for risk assessment, such as predicting the likelihood of loan default. It can handle both classification and regression tasks effectively. It relies on historical data for model training. If you have limited historical data or the data quality is poor, it may not be the best choice as it requires a sufficient amount of high-quality data for accurate model building.
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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
DataRobot helps, with algorithms, to analyze and decipher numerous machine-learning techniques in order to provide models to assist in company-wide decision making.
Our DataRobot program puts on an "even playing field" the strength of auto-machine learning and allows us to make decisions in an extremely timely manner. The speed is consistent without being offset by errors or false-negatives.
It encompasses many desired techniques that help companies in general, to reconfigure in to artificial intelligence driven firms, with little to no inconvenience.
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.
The platform itself is very complicated. It probably can't function well without being complicated, but there is a big training curve to get over before you can effectively use it. Even I'm not sure if I'm effectively using it now.
The suggested model DataRobot deploys often not the best model for our purposes. We've had to do a lot of testing to make sure what model is the best. For regressive models, DataRobot does give you a MASE score but, for some reason, often doesn't suggest the best MASE score model.
The software will give you errors if output files are not entered correctly but will not exactly tell you how to fix them. Perhaps that is complicated, but being able to download a template with your data for an output file in the correct format would be nice.
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.
DataRobot presents a machine-learning platform designed by data scientists from an array of backgrounds, to construct and develop precise predictive modeling in a fraction of the time previously taken. The tech invloved addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot utilizes parallel processing to evaluate models in R, Python, Spark MLlib, H2O and other open source databases. It searches for possible permutations and algorithms, features, transformation, processes, steps and tuning to yield the best models for the dataset and predictive goal.
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
As I am writing this report I am participating with Datarobot Engineers in an complex environment and we have their whole support. We are in Mexico and is not common to have this commitment from companies without expensive contract services. Installing is on premise and the client does not want us to take control and they, the client, is also limited because of internal IT regulations ,,, soo we are just doing magic and everybody is committed.
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
I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the model from there and fine tune further if you need.
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
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