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
Dataiku DSS
Score 9.1 out of 10
N/A
Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.
Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.
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.
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.
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.
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
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
