Most Helpful Review
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
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.
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.
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.
The thing that I find most valuable is the ability to clean the data.
The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly.
Imageflow is a visual tool that helps make it easier for business people to understand complex workflows.
I haven't heard about any major stability issues. At this time I feel like it's stable.
The time travel feature is the solution's most valuable aspect.
I work in the data science field and I found Databricks to be very useful.
The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient.
Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great.
Automation with Databricks is very easy when using the API.
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.
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.
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.
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 product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment.
The product needs samples and templates to help invite users to see results and understand what the product can do.
Pricing is one of the things that could be improved.
Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with.
It would be very helpful if Databricks could integrate with platforms in addition to Azure.
The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets.
It should have more compatible and more advanced visualization and machine learning libraries.
Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems.
Pricing and Cost Advice
I believe our cost is $1,000 per month.
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.
Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery.
I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly.
Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful.
out of 33 in Data Science Platforms
Average Words per Review
out of 33 in Data Science Platforms
Average Words per Review
Compared 55% of the time.
Compared 45% of the time.
Compared 18% of the time.
Compared 15% of the time.
Compared 7% of the time.
Also Known As
|Databricks Unified Analytics, Databricks Unified Analytics Platform|
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.
Databricks creates a Unified Analytics Platform that accelerates innovation by unifying data science, engineering, and business. It utilizes Apache Spark to help clients with cloud-based big data processing. It puts Spark on “autopilot” to significantly reduce operational complexity and management cost. The Databricks I/O module (DBIO) improves the read and write performance of Apache Spark in the cloud. An increase in productivity is ensured through Databricks’ collaborative workplace.
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|Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware|
Comms Service Provider64%
Financial Services Firm10%
Software R&D Company4%
Software R&D Company44%
Comms Service Provider6%