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Darwin pros and cons

Vendor: SparkCognition
4.0 out of 5
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Pros & Cons summary

Darwin enhances efficiency by reducing model processing time and simplifies data usage post-training. It assesses dataset quality, automating model generation to save time. Despite lacking a direct API and a time-consuming analyze function, it streamlines tasks effectively. Preparing datasets poses challenges, and while it delivers initial results, further analysis is needed. Improvements are required in tutorials and sample datasets for better understanding.

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Prominent pros & cons

PROS

Darwin significantly increases efficiency and productivity by reducing model processing time from days to minutes.
Users find Darwin simple to use once they are trained on the model with flexibility in how it is utilized.
Automatic assessment of dataset quality, including identification of missing data points or incorrect data types, is highly valuable.
The model-generation feature allows users to create effective models quickly without trial and error.
Darwin streamlines low-level data science work and performs automatic data checking for viability.

CONS

Users struggle with downloading data from AWS to use in Darwin due to the lack of a direct API connection.
The process of operationalizing and industrializing models, such as unique credit models, is challenging with Darwin, partly due to data set preparation issues.
The analyze function in Darwin is time-consuming, causing delays in the modeling process.
Darwin's automatic quality assessment of datasets is considered insufficient, often requiring additional manual analysis.
Documentation, tutorials, and sample data sets for Darwin are seen as inadequate for understanding and implementing the product efficiently.
 

Darwin Pros review quotes

AC
Jun 11, 2021
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.
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NC
Dec 4, 2019
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.
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JJ
Dec 10, 2019
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.
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Darwin
December 2024
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reviewer1244334 - PeerSpot reviewer
Dec 5, 2019
The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types.
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EC
Nov 28, 2019
The thing that I find most valuable is the ability to clean the data.
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reviewer1244550 - PeerSpot reviewer
Dec 5, 2019
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.
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WC
Dec 5, 2019
In terms of streamlining a lot of the low-level data science work, it does a few things there.
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TK
Dec 4, 2019
I find it quite simple to use. Once you are trained on the model, you can use it anyway you want.
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Darwin Cons review quotes

AC
Jun 11, 2021
There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do.
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NC
Dec 4, 2019
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.
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JJ
Dec 10, 2019
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.
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Buyer's Guide
Darwin
December 2024
Free Report: Darwin Reviews and More
Learn what your peers think about Darwin. Get advice and tips from experienced pros sharing their opinions. Updated: December 2024.
DOWNLOAD NOW
824,067 professionals have used our research since 2012.
reviewer1244334 - PeerSpot reviewer
Dec 5, 2019
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.
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EC
Nov 28, 2019
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.
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reviewer1244550 - PeerSpot reviewer
Dec 5, 2019
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.
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WC
Dec 5, 2019
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.
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TK
Dec 4, 2019
The analyze function takes a lot of time.
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Product Categories

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Popular Comparisons

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