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."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."
"The virtual environment is very good."
"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."
"I can use Anaconda for non-heavy tasks."
"The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors."
"The most advantageous feature is the logic building."
"The documentation is excellent and the solution has a very large and active community that supports it."
"The notebook feature is an improvement over RStudio."
"ML Studio is very easy to maintain."
"The visualizations are great. It makes it very easy to understand which model is working and why."
"One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."
"The solution is scalable."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"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."
"When you install Anaconda for the first time, it's really difficult to update it."
"The solution would benefit from offering more automation."
"I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."
"A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area."
"It also takes up a lot of space."
"The interface could be improved. Other solutions, like Visual Studio, have much better UI."
"I think it should be made cheaper for certain people…It may appear costlier for those who don't consider time important."
"The solution should be more customizable. There should be more algorithms."
"The platform's integration feature could be better."
"The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."
"In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions."
"I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
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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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