Removing Barriers to AI Adoption with dotData 2.0
A fundamental problem that prevents AI from reaching the broad market is the complexity of this technology, the challenges around its implementation, and usability. To enable more people to embrace AI, we must lower the barriers to AI adoption. Software vendors should build platforms that make AI simple to use. Enterprises customers need the right set of tools for business, operations, or LOB users. Tools that make using AI as easy as a drag and drop operation. The majority of existing AI platforms are designed for the experienced data science professionals. But what about the non-data science community? If you are a citizen data scientist such as BI developer or business analyst and would like to infuse AI in your applications, very limited options are available.
That changes with dotData 2.0, a platform designed for BI and analytics professionals.
So what’s new in dotData 2.0? In addition to significant UX upgrades, key updates include auto-balancing of accuracy and transparency, more accurate and interpretable auto-designed features, expanded out of the box connectivities and seamless model porting with dotDataPy and dotData Stream:
- Simple, Intuitive UI: Significantly simplified interface and workflow enable BI professionals to execute the entire ML/AI development process in just five minutes. The overall flow is very easy to understand and convenient to navigate to other sections. With automated model building, the development process is fully automated requiring no coding or manual feature generation. The new UI makes it very easy to explain prediction, identify and confirm important features.
- Seamless AI + BI Experience: The new release gives a seamless BI with AI experience through out-of-box connectivities with third-party data and business Intelligence platforms including Tableau, Teradata, and MS SQL / Azure database. The platform is flexible to work with self-service data preparation tools, acting as the core analytics module that connects with popular BI tools such as Tableau or Power BI for visualization.
- Enhanced Interpretability & Accuracy: With dotData 2.0, the core engine automates the process to explore simpler ML models with minimal change to accuracy, in addition to pursuing the model for the highest accuracy. This enables users to balance accuracy and interpretability based on their business requirements. Version 2.0 introduces various improvements in its AI-powered feature engineering to produce more accurate and more interpretable features such as advanced categorical encoding, more natural feature explanation, and more robust feature selection to avoid multicollinearity.
- Flexible Deployment: With the new release, dotData Stream is GA. Stream is a new containerized AI/ML model that enables real-time predictive capabilities for dotData users. Stream is highly scalable and effective – a single prediction can be performed as fast as tens of milliseconds or even faster for micro-batch predictions. The deployment is a one-click operation, as simple as launching a docker container with AI/ML models downloaded from dotData platform. An end-point for real-time predictions becomes immediately available. In addition, dotData Stream can run in the cloud MLOps platforms for enterprise AI/ML orchestration or at the edge servers for intelligent IoT applications.
Moreover, features and models developed using Version 2.0 are deployable both on dotData Py as customizable Python end-points and on dotData Stream as real-time stream end-points.
These new updates significantly simplify AI and ML experience for citizen data scientists, augment analytical performance, and improve interpretability through AutoML 2.0.
Curious about dotData 2.0, check out our AI FastStart program here or schedule a demo to learn more.