We started with small projects for then, take more complex use cases in other business …
- The platform alleviates the cumbersome and lengthy former process associated with model design and formulation.
- It streamlines multiple algorithms that can be tested and used to validate results.
- Simple interface that allows modeling, simulation, and sensitivity analysis both from an operational …
- DataRobot has helped us navigate a harsh and hostile environment, given high uncertainty and ambiguity regarding loan decision-making.
- DataRobot's platform has saved loads of time and effort that would otherwise have been devoted to formulation, programming, and algorithm definition.
- DataRobot has …
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- Automated Machine Learning (41)9.393%
- Automatic Data Format Detection (40)8.282%
- Visualization (41)7.979%
- Interactive Data Analysis (40)7.878%
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- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
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The solutions include tools providing data preparation enabling users to explore and shape data in preparation for machine learning, automate machine learning, deploy, monitor, manage, and govern all AI models (i.e. MLOps), and the ability to generate time series models that predict the future values of a data series based on its history and trend.
DataRobot AI Cloud platform extends the user's data science expertise with automation and aims to give unlimited flexibility for both data science experts and non-technical users to succeed with AI.
- Supported: Connect to Multiple Data Sources
- Supported: Extend Existing Data Sources
- Supported: Automatic Data Format Detection
- Supported: MDM Integration
- Supported: Visualization
- Supported: Interactive Data Analysis
- Supported: Interactive Data Cleaning and Enrichment
- Supported: Data Transformations
- Supported: Data Encryption
- Supported: Built-in Processors
- Supported: Multiple Model Development Languages and Tools
- Supported: Automated Machine Learning
- Supported: Single platform for multiple model development
- Supported: Self-Service Model Delivery
- Supported: Flexible Model Publishing Options
- Supported: Security, Governance, and Cost Controls
- Supported: Automated Time Series
- Supported: Cloud-Hosted Notebooks
- Supported: Data Preparation
- Supported: Feature discovery
- Supported: MLOps
- Supported: No Code AI App Builder
- Supported: AI Apps
- Supported: Decision Flows
- Supported: Bias Testing and Monitoring
- Supported: Compliance Documentation and Prediction Explanations
- Supported: Anomaly Detection
- Supported: Data Prep Automation
- Supported: Bringing together any type of data from any source
- Supported: Demand Forecasting
|Deployment Types||On-premise, Software as a Service (SaaS), Cloud, or Web-Based|
|Operating Systems||Windows, Linux, Mac|
|Supported Languages||English, Spanish, French, Korean, Japanese, Portuguese|
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- Manages messy data.
- Evaluates numerous models with little user effort
- Provides reliable repository for data and models.
- DataRobot is NOT the limiting factor in the improvement of my business process. Getting more and cleaner data is the problem, so, in a sense, DataRobot cannot be improved for my purposes.
- Time Series
- Awesome robot predictions
- Flexibility of certain models
- Time series included in general base package
- Certain data source integration
- Feature Engineering
- Model Training
- Metric Validation
- Detect Bias
- Direct Connection to Oracle Database
- Normalize Data
- Customer Success
- Minimizes coding extensively (for the user).
- Allows high levels of customization.
- Deployments are well organized.
- Model training could be faster.
- Too many limitations, such as making changes to deployments (such as threshold).
- Provides Charts that show how well their model performs,
- Is highly customizable when you're building a model.
- Makes a lot of the decisions for you so you don't have to babysit each step.
- 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.
- Process large amounts of data quickly.
- Provides very accurate predictions.
- It provides an easy way to compare the predictive value of each of the features of the model.
- Has a good text analysis tool.
- Further improvements to their text analysis tool, to be more like the Qualtrics text analysis tool, would be a great addition. Qualtrics has templates built into their text analysis tool for customer service, quality control, etc, and will automatically slot your text responses into categories associated with certain sub areas of those larger categories.
- Iterative model development
- Fast training of a very large number of models
- Easy deployment to their cloud solution, or export as an approximate model
- Visualization and explanation of important model components
- We should be able to download data sets from our own projects--after all, we uploaded them originally (and they were not stored locally; they were created specifically for a DataRobot project).
- The sales team is very aggressive at pushing features that we would never use, such as data hygiene (clunky integration of Paxata), ML Ops (just don't need it), and AI services (we're a mature company; we don't need help coming up with use cases).
- Pricing changes every year--not just the amount but what you actually get, so we need to nitpick the contract each year because DataRobot has inevitably eliminated something we need.
- Powerful to quickly connect and process data
- Excellent algorithm generation and model building capabilities
- Superior performance to deploy live models with API connectivity
- Enhanced capabilities to connect to SQL servers is desired.
- Functionalities oriented to suggest feature improvements and amendments when models decay over time.
- Formatting is an issue. Standardization and user-friendliness are required whenever working with local spreadsheets, CSVs, and other data repositories.
It is well suited to carry out forecasts and feed from massive amounts of data to simplify decision-making in a seamless, comprehensive, and applicable manner.
- Feature engineering
- Time series
- An on premise solution instead of cloud for use with very sensitive data
- Integration with MS Azure
- Feature engineering
- Performance and velocity estimation
- Overall management of several models at once
- Integration with external data sources could be easier
- Easier than others
- Well explained
- Courses in Spanish
- Not always intuitive
- Customer support
- Customer scaling up
- Give visibility on what other customers are doing in other industries
- Data Science ops
- Auto ML
- yet to find a good example
- Automated machine learning
- Measuring feature impacts and effects
- Producing live probability scores
- Error notification - it can be challenging to identify the cause of errors
- Exporting data from the GUI is not possible
- Complicated commercials which regularly change
- Automated modelling
- Model deployment
- Deployment monitoring
- Time Series modelling
- Integration between cloud-based solution and on-premise data
- More support for NLP
- User Interface
- Suitable for extremely unexperienced users
- Pricing Model
- Information on in production solutions at similar customers
- It can do feature engineering very well.
- It can explain the model and model predictions very well.
- The deployment and model management is very easy.
- It tries exhaustive list of models before finalizing one.
- It does not provide enough opportunity to modify the pipeline.
- Once the control is given to datarobot, there is little that a data scientist can do.
- DataRobot can generate explanation for why a model was
- Model retraining automation is not very flexible in Datarobot
- Hyper parameter tuningoptimization
- Feature generation
- Improving model accuracy metrics
- Improve on Automation
- Price points can be improved
- Quickly solving data science problems
- Monitoring models in production
- Compare different approaches in solving a problem with AI
- Integrating the capability to modify with python the whole pipeline
- Fantastic data scientists
- solving the problemet
- not so easy to use when you want a model in production
- timeseries data analyse
- Platform simple and intuitive.
- Wonderful support.
- Product roadmap well managed.
- Socializing and promoting how their customers are using the platform and deriving value or making an impact.
- Use of keys words
- Accurate prediction of ticket classes
- Integrates well with UiPath RPA
- The user interface can be improved
- Add multi factor authentication to improve security
- Change the pricing structure to make it more cost effective
- Validated algorithms with a wide, composite range
- Transparent analysis
- Excellent documentation
- User friendly, interactive visualisations
- Connection to Tableau
- Dark background interface is harder to read
- Better explainability of features - feature importance vs feature impact etc.
- Need for an App for what- if analysis
- Easy of access
- Customized dashboard
- Ease of integration
- Support multiple data formats
- Various templates and reports available
- Data center in India
- Detailed Audit trail
- Integration with inhouse applications
- It's a magnificent software with business intelligence.
- It's excellent with instant access of data.
- Efficient for it's ease of deployment.
- Intuitive UI.
- It requires programming skills to use.
- It's magnificent with being able to collect and manipulate database.
- Excellent for tracking data with maximum protection.
- It's great for business intelligence.