How Will Automation Change Enterprise Data Science? – Part 2
continued from last week’s post…
dotData, Data Science Without The Headaches
dotData is a brand new breed of AutoML product that provides what we call Full Cycle Data Science Automation. At the heart of our vision is the idea that the data science process should be fast, easy to perform, and easy to analyze and deploy, from raw business data to the business values. Our vision has led us to develop dotData Enterprise and dotData Py, two related platforms that leverage the same automation engine in uniquely different ways. dotData Enterprise is ideal for the citizen data scientist: fully automated, point-and-click driven, and ready to automate 100% of the data science process without requiring in-depth knowledge of how data science works. dotData Py, on the other hand, is ideal for data scientists. dotData Py provides a python library for Jupyter notebooks, one of the most popular data science platforms available.
With dotData Enterprise, citizen-data scientists can work on data science projects without having to learn how to become full-fledged data scientists.
With dotData Py, data scientists can leverage the benefits of automated feature engineering to dramatically shorten development times, while still retaining the high degree of control and customization that their job requires.
Four Pillars to Change Data Science
dotData helps enterprise organizations accelerate their adoption and monetization of Artificial Intelligence (AI) and machine learning (ML) projects. Our full-cycle data science automation accelerates every step of the process, including the data wrangling and feature engineering that often takes months to complete. With dotData, the data science team can execute 10x more projects. Data science is eventually test-and-learn, and the significantly-short turnaround allows you to find critical use cases faster.
dotData’s platform is designed to take the hard part of the data science process and automate it. With dotData, a more comprehensive range of people like BI engineers or business analysts can execute and contribute to data science projects, which genuinely democratizes and scale-out data science in the organization. Further, by leveraging “citizen” data scientists for common use cases, data scientists can focus on higher-impact and more challenging tasks.
dotData’s AI-powered feature engineering can explore millions of feature hypotheses for a given use case. The automation augments the ability of data scientists and even domain experts to test many more hypotheses than ever before and delivers new business insights through transparent features.
dotData automatically produces production-ready feature-generating pipeline and ML scoring models and operationalizes them through dotData APIs. The implementation is as simple as adding one line of code, and even more importantly, the maintenance of the entire production workflows, i.e., retraining features and ML models, is also automated.
AutoML Results That Speak For Themselves
How does the whole process work in real-life environments? dotData has been able to accelerate the AI and machine learning production of global companies like Japan Airlines and SMBC Financial Services and has reduced development efforts from months to days. In fact, in a recent test with a global, fortune 50 consumer electronics company, dotData was able to replicate AI projects that had taken five months each to complete in less than three days. dotData clients see a return on their investment in a matter of days and can finally begin to reduce the high failure rates that have plagued AI and ML shops, and that continues to limit the promise of AI. To learn more about dotData and our products, contact our sales team at firstname.lastname@example.org, or visit our website at dotdata.com.
The Right Tools For The Right People
A final step in accelerating data science is to create AutoML solutions that provide the right working environment for the right individual. The notion of empowering “citizen data scientists” is not new. The idea that, with automation, non-data scientist trained users like BI analysts can contribute to the AI and machine learning process is not new. The problem, however, is that we must provide the right tools to the right individuals. While fully automated, GUI-driven solutions are ideal for anyone not very familiar with the data science process; data scientists prefer to work within coding environments they love. For any AutoML solution to provide the right degree of automation for both citizen data scientists as well as for data scientists, the automation tools must be available in forms that can be easily deployed in the development process of each.