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
GoCD
Score 8.0 out of 10
N/A
GoCD, from ThoughtWorks in Chicago, is an application lifecycle management and development tool.
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TeamCity
Score 7.1 out of 10
N/A
TeamCity is a continuous integration server from Czeck company JetBrains.
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.
TeamCity is very quick and straightforward to get up and running. A new server and a handful of agents could be brought online in easily under an hour. The professional tier is completely free, full-featured, and offers a huge amount of growth potential. TeamCity does exceptionally well in a small-scale business or enterprise setting.
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.
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 customization is still fairly complex and is best managed by a dev support team. There is great flexibility, but with flexibility comes responsibility. It isn't always obvious to a developer how to make simple customizations.
Sometimes the process for dealing with errors in the process isn't obvious. Some paths to rerunning steps redo dependencies unnecessarily while other paths that don't are less obvious.
TeamCity runs really well, even when sharing a small instance with other applications. The user interface adequately conveys important information without being overly bloated, and it is snappy. There isn't any significant overhead to build agents or unit test runners that we have measured.
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.
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.
TeamCity is a great on-premise Continuous Integration tool. Visual Studio Team Services (VSTS) is a hosted SAAS application in Microsoft's Cloud. VSTS is a Source Code Repository, Build and Release System, and Agile Project Management Platform - whereas TeamCity is a Build and Release System only. TeamCity's interface is easier to use than VSTS, and neither have a great deployment pipeline solution. But VSTS's natural integration with Microsoft products, Microsoft's Cloud, Integration with Azure Active Directory, and free, private, Source Code repository - offer additional features and capabilities not available with Team City alone.
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
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
TeamCity has greatly improved team efficiency by streamlining our production and pre-production pipelines. We moved to TeamCity after seeing other teams have more success with it than we had with other tools.
TeamCity has helped the reliability of our product by easily allowing us to integrate unit testing, as well as full integration testing. This was not possible with other tools given our corporate firewall.
TeamCity's ability to include Docker containers in the pipeline steps has been crucial in improving our efficiency and reliability.
