DataRobot vs. Vertex AI

DataRobot can be used for risk assessment, such as predicting the likelihood of loan default. It can handle both classification and regression tasks effectively. It relies on historical data for model training. If you have limited historical data or the data quality is poor, it may not be the best choice as it requires a sufficient amount of high-quality data for accurate model building.

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Vertex AI seems to be a lot more accurate with image editing versus other competitors (including free one). We do a lot of image creation, especially of dogs in very certain scenarios. We use Adobe Stock to get us started, but many times we need some very specific edits done to the image. We've found Vertex can produce those with a lot more precision than other AI image generators.

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• DataRobot helps, with algorithms, to analyze and decipher numerous machine-learning techniques in order to provide models to assist in company-wide decision making.
• Our DataRobot program puts on an "even playing field" the strength of auto-machine learning and allows us to make decisions in an extremely timely manner. The speed is consistent without being offset by errors or false-negatives.
• It encompasses many desired techniques that help companies in general, to reconfigure in to artificial intelligence driven firms, with little to no inconvenience.

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• Vertex AI comes with support for LOTs of LLMs out of the box
• MLOps tools are available that help to standardize operational aspects
• Document AI is an out of the box feature that works just perfectly for our use cases of automating lots to tedious data extraction tasks from images as well as papers

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• The platform itself is very complicated. It probably can't function well without being complicated, but there is a big training curve to get over before you can effectively use it. Even I'm not sure if I'm effectively using it now.
• The suggested model DataRobot deploys often not the best model for our purposes. We've had to do a lot of testing to make sure what model is the best. For regressive models, DataRobot does give you a MASE score but, for some reason, often doesn't suggest the best MASE score model.
• The software will give you errors if output files are not entered correctly but will not exactly tell you how to fix them. Perhaps that is complicated, but being able to download a template with your data for an output file in the correct format would be nice.

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• Customization of AutoML models - A must needed capability to be able to tweak hyperparameters and also working with different models
• Model Explainability -Providing more comprehensive explanations about how models are utilizing features could be very beneficial
• Model versioning and experiments tracking - Enhancing the versioning capability could be good for end users

Incentivized

Invited by TrustRadius (TR) on Vendor's Behalf

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DataRobot presents a machine-learning platform designed by data scientists from an array of backgrounds, to construct and develop precise predictive modeling in a fraction of the time previously taken. The tech invloved addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot utilizes parallel processing to evaluate models in R, Python, Spark MLlib, H2O and other open source databases. It searches for possible permutations and algorithms, features, transformation, processes, steps and tuning to yield the best models for the dataset and predictive goal.

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Independently Invited by TrustRadius

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It's not always instant, but understandable when it's under heavy load. It's not impressive nor disappointing, just what is expected. But when calling this platform through API's for it to do the actions requested there is minimal delay and wait time. It feels very responsive and quick when integrating it with a call center chat platform for example.

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As I am writing this report I am participating with Datarobot Engineers in an complex environment and we have their whole support. We are in Mexico and is not common to have this commitment from companies without expensive contract services. Installing is on premise and the client does not want us to take control and they, the client, is also limited because of internal IT regulations ,,, soo we are just doing magic and everybody is committed.

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I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the model from there and fine tune further if you need.

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Vertex AI is much more accessible to non-developers than IBM's product. Moreover, Vertex AI integrates well with other Google products, enhancing its capabilities. A big plus is its integration with cloud storage, that allows for better management and access of data. In all honesty, it wasn't much of a difficult choice to choose Vertex AI.

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• We have been able to cut costs by not buying leads that we will not be able to sell on
• We have been able to deploy loan eligibility reporting which brought in new business
• We have been able to improve the performance of our credit providers and our partners which has helped to retain business

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• It is pay as you go model so it'll save more cost of your org. In our case previously we used to incurred 1-2L/Month now we are reduced it to 80k-1L.
• It'll help you save your model training & model selection time as it provides pre-trained models in autoML.
• It'll help you in terms of Security wherein we can use row level security access to authorized persons.

Incentivized

Invited by TrustRadius (TR) on Vendor's Behalf

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info