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
IBM watsonx.governance
Score 9.1 out of 10
N/A
The more AI is embedded into daily workflows, the more proactive governance is required to drive responsible, ethical decisions across the business. Watsonx.governance is used to direct, manage, and monitor an organization’s AI activities, and employs software automation to strengthen the user's ability to mitigate risk, manage regulatory requirements and address ethical concerns without the excessive costs of switching data science platforms—even for models developed using third-party tools.
We have been able to make the right decisions based on performance metrics. Data assets across the enterprise have experienced significant growth from comprehensive audits that drive quality growth. The platform has filtered out poorly analyzed data from the workflow chain and introduced stable control mechanisms that meet compliance policies.
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.
Research data can be handled and governed more effectively to save time and minimize errors. Practical learning helps students become more marketable to employers by giving them practical experience with industry-standard tools. Updates content on AI governance in courses to make them more appealing to students. Lowers the time needed to manually check for biases, increasing the validity of research findings.
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.
With its smooth integrations with different AI models and strong compliance tools, IBM watsonx.governance leads in comprehensive data governance. IBM watsonx.governance provides a well-balanced combination of governance, compliance, and integration capabilities in contrast to Dataiku, which concentrates more on data science workflows, and Holistic AI, which stresses AI ethics and risk management. That was my choice because of its robust integration features and comprehensive approach.
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
It has massively cut down the time our compliance teams spent on preparing compliance packs for EU emissions report. We're talking 4 weeks of manual tracing and spreadsheet validations to just under 3 days now!
IBM watsonx.governance flags anomalies in shipping data 2 weeks earlier than our older system, saving us thousands by renegotiating contracts before spot prices rise
