Compare Amazon SageMaker vs. Darwin

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Amazon SageMaker Logo
12,082 views|10,231 comparisons
Darwin Logo
619 views|172 comparisons
Most Helpful Review
Find out what your peers are saying about Amazon SageMaker vs. Darwin and other solutions. Updated: May 2021.
501,151 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"The few projects we have done have been promising.""They are doing a good job of evolving.""The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases.""Allows you to create API endpoints.""The deployment is very good, where you only need to press a few buttons."

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"The thing that I find most valuable is the ability to clean the data.""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 find it quite simple to use. Once you are trained on the model, you can use it anyway you want.""The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types.""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 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.""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.""In terms of streamlining a lot of the low-level data science work, it does a few things there."

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Cons
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time.""I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox.""AI is a new area and AWS needs to have an internship training program available.""Lacking in some machine learning pipelines.""Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."

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"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.""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.""The analyze function takes a lot of time.""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.""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.""There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do.""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.""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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Pricing and Cost Advice
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation.""The support costs are 10% of the Amazon fees and it comes by default."

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"The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well.""In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos.""As far as I understand, my company is not paying anything to use the product.""I believe our cost is $1,000 per month."

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501,151 professionals have used our research since 2012.
Ranking
13th
Views
12,082
Comparisons
10,231
Reviews
4
Average Words per Review
510
Rating
7.5
9th
Views
619
Comparisons
172
Reviews
7
Average Words per Review
1,581
Rating
8.4
Popular Comparisons
Also Known As
AWS SageMaker, SageMaker
Learn More
Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.

Offer
Learn more about Amazon SageMaker
Learn more about Darwin
Sample Customers
DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Hunt Oil, Hitachi High-Tech Solutions
Top Industries
VISITORS READING REVIEWS
Computer Software Company27%
Media Company17%
Comms Service Provider12%
Financial Services Firm7%
VISITORS READING REVIEWS
Comms Service Provider48%
Financial Services Firm12%
Computer Software Company9%
Government6%
Company Size
REVIEWERS
Midsize Enterprise57%
Large Enterprise43%
REVIEWERS
Small Business75%
Large Enterprise25%
Find out what your peers are saying about Amazon SageMaker vs. Darwin and other solutions. Updated: May 2021.
501,151 professionals have used our research since 2012.

Amazon SageMaker is ranked 13th in Data Science Platforms with 5 reviews while Darwin is ranked 9th in Data Science Platforms with 8 reviews. Amazon SageMaker is rated 7.6, while Darwin is rated 8.4. The top reviewer of Amazon SageMaker writes "A solution with great computational storage, has many pre-built models, is stable, and has good support". 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". Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio, H2O.ai and Domino Data Science Platform, whereas Darwin is most compared with H2O.ai and IBM Watson Studio. See our Amazon SageMaker vs. Darwin report.

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We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.