The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to drive business outcomes, that is available on the user's cloud platform-of-choice, on-premise, or as a fully-managed service. The solutions include tools providing data preparation enabling users to explore and…
$0
NVIDIA RAPIDS
Score 9.1 out of 10
N/A
NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs. Users can run data science workflows with high-speed GPU compute and parallelize data loading, data manipulation, and machine learning for 50X faster end-to-end data science pipelines.
N/A
Pricing
DataRobot
NVIDIA RAPIDS
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
DataRobot
NVIDIA RAPIDS
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
Yes
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
—
—
More Pricing Information
Community Pulse
DataRobot
NVIDIA RAPIDS
Features
DataRobot
NVIDIA RAPIDS
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
DataRobot
-
Ratings
NVIDIA RAPIDS
9.1
2 Ratings
8% above category average
Connect to Multiple Data Sources
00 Ratings
9.62 Ratings
Extend Existing Data Sources
00 Ratings
8.82 Ratings
Automatic Data Format Detection
00 Ratings
9.02 Ratings
MDM Integration
00 Ratings
9.01 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
DataRobot
-
Ratings
NVIDIA RAPIDS
9.4
2 Ratings
11% above category average
Visualization
00 Ratings
9.42 Ratings
Interactive Data Analysis
00 Ratings
9.42 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
DataRobot
-
Ratings
NVIDIA RAPIDS
8.9
2 Ratings
9% above category average
Interactive Data Cleaning and Enrichment
00 Ratings
7.82 Ratings
Data Transformations
00 Ratings
9.42 Ratings
Data Encryption
00 Ratings
9.01 Ratings
Built-in Processors
00 Ratings
9.42 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
DataRobot
-
Ratings
NVIDIA RAPIDS
9.2
2 Ratings
9% above category average
Multiple Model Development Languages and Tools
00 Ratings
9.01 Ratings
Automated Machine Learning
00 Ratings
9.42 Ratings
Single platform for multiple model development
00 Ratings
9.42 Ratings
Self-Service Model Delivery
00 Ratings
9.01 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
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.
NVIDIA RAPIDS drastically improves our productivity with near-interactive data science. And increases machine learning model accuracy by iterating on models faster and deploying them more frequently. It gives us the freedom to execute end-to-end data science and analytics pipelines.
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.
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.
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.
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.
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.
RAPIDS GPU accelerates machine learning to make the entire data science and analytics workflows run faster, also helps build databases and machine learning applications effectively. It also allows faster model deployment and iterations to increase machine learning model accuracy. The great value of money.