Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
$0
per month
CircleCI
Score 9.5 out of 10
N/A
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
GoCD
Score 8.0 out of 10
N/A
GoCD, from ThoughtWorks in Chicago, is an application lifecycle management and development tool.
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.
Previously, our team used Jenkins. However, since it's a shared deployment resource we don't have admin access. We tried GoCD as it's open source and we really like. We set up our deployment pipeline to run whenever codes are merged to master, run the unit test and revert back if it doesn't pass. Once it's deployed to the staging environment, we can simply do 1-click to deploy the appropriate version to production. We use this to deploy to an on-prem server and also AWS. Some deployment pipelines use custom Powershell script for.Net application, some others use Bash script to execute the docker push and cloud formation template to build elastic beanstalk.
User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.
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.
Pipeline-as-Code works really well. All our pipelines are defined in yml files, which are checked into SCM.
The ability to link multiple pipelines together is really cool. Later pipelines can declare a dependency to pick up the build artifacts of earlier ones.
Agents definition is really great. We can define multiple different kinds of environments to best suit our diverse build systems.
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.
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.
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.
It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.
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.
GoCD is easier to setup, but harder to customize at runtime. There's no way to trigger a pipeline with custom parameters.
Jenkins is more flexible at runtime. You can define multiple user-provided parameters so when user needs to trigger a build, there's a form for him/her to input the parameters.
Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details
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.
Settings.xml need to be backed up periodically. It contains all the settings for your pipelines! We accidentally deleted before and we have to restore and re-create several missing pipelines
More straight forward use of API and allows filtering e.g., pull all pipelines triggered after this date
