We performed a comparison between Anaconda and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The most valuable feature is the Jupyter notebook that allows us to write the Python code, compile it on the fly, and then look at the results."
"The documentation is excellent and the solution has a very large and active community that supports it."
"The most valuable feature is the set of libraries that are used to support the functionality that we require."
"The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly."
"The virtual environment is very good."
"I can use Anaconda for non-heavy tasks."
"It helped us find find the optimal area for where our warehouse should be located."
"With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages."
"Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful."
"The solution is scalable."
"It's easy to deploy."
"The solution facilitates our production."
"The solution is very easy to use, so far as our data scientists are concerned."
"I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model."
"The most valuable feature is its compatibility with Tensorflow."
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
"Anaconda should be optimized for RAM consumption."
"A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area."
"I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."
"When you install Anaconda for the first time, it's really difficult to update it."
"The solution would benefit from offering more automation."
"Having a small guide or video on the tool would help learn how to use it and what the features are."
"Anaconda can't handle heavy workloads."
"The interface could be improved. Other solutions, like Visual Studio, have much better UI."
"Integration with social media would be a valuable enhancement."
"In terms of improvement, I'd like to have more ability to construct and understand the detailed impact of the variables on the model. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" it wasn't great."
"The initial setup time of the containers to run the experiment is a bit long."
"Microsoft Azure Machine Learning Studio could improve in providing more efficient and cost-effective access to its tools for companies like mine."
"We can create a label job, but we still have to use the Azure Machine Learning REST APIs, which are not yet supported in the Python SDK version 2."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Anaconda is ranked 13th in Data Science Platforms with 17 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 53 reviews. Anaconda is rated 8.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Anaconda writes "Offers free version and is helpful to handle small-scale workloads". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". Anaconda is most compared with Databricks, Amazon SageMaker, Microsoft Power BI, IBM SPSS Statistics and IBM Watson Studio, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Alteryx. See our Anaconda vs. Microsoft Azure Machine Learning Studio report.
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