CircleCI is a software delivery engine from the company of the same name in San Francisco, that helps teams ship software faster, offering their platform for Continuous Integration and Continuous Delivery (CI/CD). Ultimately, the solution helps to map every source of change for software teams, so they can accelerate innovation and growth.
$0
for up to 6,000 build minutes and up to 5 active users per month
DataRobot
Score8.2 out of 10
N/A
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…
Based on our experience, CircleCI is well-suited for automating mobile app release cycles. For example, to release an iOS app, you would need to build, sign, and upload it to TestFlight, which requires a dedicated Mac in the office. But with CircleCI, you can have macOS executors, so you don't have to manage a physical build machine. Another benefit is that CircleCI's certified AWS Orbs abstract away complex authentication and deployment logic, allowing us to build, push, and deploy Docker containers to Amazon ECS with minimal configuration and high reliability. CircleCI is less suited for smaller projects where the development and deployment are not that extensive, for example, a static site. Once you have built a static site, you probably won't make any further changes, so there's no point in paying for it.
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 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.
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
Automated builds! This is really why you get CircleCI, to automate the build process. This makes building your application far more reliable and repeatable. It can also run tests and verify your application is working as expected.
Simple. Unlike Jenkins, Teamcity, or other platforms, CircleCI doesn't need a lot of setup. It's completely hosted, so there's no infrastructure to set up. The config file does take a bit to understand, but if you follow their example and start with something small and add to it, you can get it up and going quicker than it first looks.
Scales easily. Again, since it's all cloud-based, you don't have to manage or scale infrastructure. Simply subscribe to the number of containers you want, and scaling up just means buying more containers.
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
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
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
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
The reliability & speed, it just works. The ability to spin up macOS runners and Docker containers on demand without managing hardware is a huge win. The Orbs system makes integrating with AWS and Slack incredibly easy, saving us weeks of custom scripting and providing real-time updates in our Slack channel. This makes it easy for us to track and ensures that everyone involved knows the status. Of course, it has drawbacks related to configuration complexity and, in some cases, cost transparency, but overall, it is an industry-standard, robust tool that solves our core infrastructure problems well.
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
It's pretty snappy, even with using workflows with multiple steps and different docker images. I've seen builds take a long time if it's really involved, but from what I can tell, it's still at least on par if not faster than other build 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
Unless you have a reasonably large account, you're going to be mainly stuck reading their documentation. Which has improved somewhat over the years but is still extremely limited compared to a platform like Digital Ocean who invested in the documentation and a community to ensure it's kept up to date. If you can't find your answer there, you can be stuck.
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
Jenkins is usually self-hosted, Travis CI's infrastructure is largely unreliable (lots of tests time out for no discernable reason), and Semaphore encourages you to configure your CI/CD from a web UI. We like CircleCI because its hosted, our tests run largely as expected on their infrastructure, and we can configure it from a config file that we track in GitHub.
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
We pay over $5K/ month and we have high expectations for service. Sometimes I feel that we don't get the value, but only sometimes.
We have had to build our own application to keep state and broker releases and deployments. We call our app deployer. I feel that CircleCI could do more to understand our needs and possibly build additional features that would enable us to invest less in build and deployment infrastructure and justify paying more for Circle.
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
