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
Google Gemini
Score 8.7 out of 10
N/A
Google Gemini (formerly Bard) is an AI assistant, presented as a creative and helpful collaborator. Gemini for Workspace is available via two plans: a Gemini Enterprise add-on, and a Gemini Business add-on.
Gemini is well suited to help in customer service, to create summaries of emails sent by customers, generating possible responses to them, rephrasing communications, help create and then correct SQL queries, interpreting responses, it's not so good if you need to help with a sensitive topic due to it taking personally identifying information
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.
Deep research for getting first business research draft from Gemini, post which i use series of prompts to improve it and use my understanding to refine it further
Canvas to produce structured business topic research and newsletter. Direct edits to the sections and making client ready reports
Learning mode to get help on step by step automation of AI workflows
Currently the document database caps out at 10, requiring us to condense some of our policies
It's large context window is a blessing and a curse. Sometimes it stops generating half way through a very ambitious request as it delivers page after page of content
There is no way to share Gems currently, so we have to publish guides to our employees on how to best configure them
Google Gemini Web UI provided an intuitive user experience with a collapsible side menu and a recent chat feature. It has a nice, clean design and easy-to-use "Ask Gemini" chat control with an integrated Tool menu that provides quick access to Deep Research and Create images options. One can also search for chats quickly and efficiently.
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.
Hootsuite's OwlyGPT is great for social listening data, but Gemini is far ahead in terms of caption writing and other writing needs. Even for content creation ideas, I'd rather take the social listening insights then feed that to Gemini. ChatGPT I truly have never been a fan of. Gemini's interface has always intrigued me more and I find it to have great functionality. Lastly, I included Perplexity - just to note another tool I've used. Perplexity is great for deep research, but outside of this I would always go with Gemini.
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