dotData is the ideal set of tools for data scientists. We were the first full-cycle data science automation platform – designed to leverage relational as well as flat-file data sources to automate feature engineering and generate thousands of features in record time.
Accelerate Your Data Science Throughput
Why Data Scientists Love dotData
Data Science Without the Headaches
Put the power of full-cycle data science to work for you. dotData helps you leverage your data sources to generate millions of features, presenting you with the most reliable ones automatically. Test against the most popular machine learning algorithms and prepare your chosen ML models with complete feature transparency in an easy to deploy process that requires only a few lines of code. dotData takes your data science process from months to days.
Tools for Data Scientists that Automate Workflow
Not all tools for data scientists are built alike. For data scientists, working in your favorite Jupyter notebook is probably your preferred method of operation. dotData gives you all the power and flexibility of our full gui-based dotData Enterprise in a python library that easily integrates into your favorite development environment.
Automate Everything, Even Feature Engineering
dotData provides you with the ability to automate 100% of your data science process. Data collection and wrangling, feature engineering, model selection, and testing, analysis, and even operationalization of your data science process can now happen in days instead of months. It’s literally data science without the headaches.
Model Deployment & Maintenance Made Easy
With dotData, deploying your AI and machine learning models is just as fast as the rest of the process. Deploy models quickly and with little risk with our powerful dotData APIs. Deploy models and update them with no downtime and no need to rescore your models.
Priced For True Scalability
dotData fosters true data science scalability and collaboration. That’s because we don’t use a per-user pricing model. dotData can be easily used by as many data scientists, analysts, or engineers as you need, without ever paying for more user names. Pay only for the actual computing power used by your models and deliver on the promise of ubiquitous data science.