We performed a comparison between Anaconda and Darwin based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."The most advantageous feature is the logic building."
"The solution is stable."
"I can use Anaconda for non-heavy tasks."
"The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly."
"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."
"It helped us find find the optimal area for where our warehouse should be located."
"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 solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."
"The most valuable feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate."
"Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision."
"I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable."
"The thing that I find most valuable is the ability to clean the data."
"The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."
"In terms of streamlining a lot of the low-level data science work, it does a few things there."
"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
"Anaconda should be optimized for RAM consumption."
"The interface could be improved. Other solutions, like Visual Studio, have much better UI."
"I think that the framework can be improved to make it easier for people to discover and use things on their own."
"Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring."
"I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."
"It crashes once in a while. In case of a reboot or something unexpected, the unseen code part will get diminished, and it relatively takes longer than other applications when a reboot is happening. They can improve in these areas. They can also bring some database software. They have software for analytics and virtualization. However, they don't have any software for the database."
"When you install Anaconda for the first time, it's really difficult to update it."
"The solution would benefit from offering more automation."
"There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."
"The analyze function takes a lot of time."
"The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition."
"An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data."
"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin."
"Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model."
"The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working it to get the data sets ready for Darwin."
"There are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets."
Earn 20 points
Anaconda is ranked 13th in Data Science Platforms with 15 reviews while Darwin is ranked 27th in Data Science Platforms. Anaconda is rated 7.8, while Darwin is rated 8.0. 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 Darwin writes "Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows". Anaconda is most compared with Databricks, Microsoft Azure Machine Learning Studio, Amazon SageMaker, Microsoft Power BI and IBM SPSS Statistics, whereas Darwin is most compared with IBM Watson Studio, Databricks and Microsoft Azure Machine Learning Studio.
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