Artificial Intelligence and Data Science

Tap the power of visual AI to accelerate your journey from data to insights at enterprise scale. Rapidly build machine learning, deep learning and graph models through intuitive visual studio, and launch with automated deployment and retraining. Infuse AI into automated processes and applications for real-time predictive and prescriptive insights to drive faster and smarter decisions. Stay ahead of the market with AI-driven innovative business models.

What’s more! Combine AI with business rules, process automation and content services to inject intelligence in applications and processes across the enterprise.

With Newgen AI Cloud, you can do it all

• **Prepare: **Leverage an intuitive visual interface to easily prepare data at a massive scale for enterprise-wide analytics and machine learning initiatives. Ensure the reliability of results by validating the data for accuracy and consistency. Furthermore, perform various data operations, such as blend, curate, and wrangle for creating and scaling data pipelines - Data Fusion Hub
• **Visualize: **Leverage different visualization techniques, including bubble charts, scatter plots, and others to explore and easily understand data. Furthermore, prepare the data for model training by detecting and repairing data anomalies, cleansing data, and performing sanity checks using machine learning-based data validation analysis - Data Visualization
• **Train: **Streamline the end-to-end data science lifecycle by leveraging an intuitive drag-and-drop interface for rapid model development, experimentation, and evaluation. Utilize in-built set of modeling algorithms to develop and deploy models on disparate datasets. Perform detailed evaluation of models through visual performance metric reports, thereby identifying, training, and optimizing for the best-fit model - Model Development Studio
• **Deploy and Monitor: **Identify the best-performing model from various available models set, using the built-in model recalibration mechanism, and ensure its single-click deployment. Perform model retraining on a predefined frequency, scheduled, and governed in production. Furthermore, compare and continuously monitor model performance while gaining detailed insights into its behavior - Deployment and Monitoring Server
• **Collaborate: **Foster enterprise-wide collaboration among multiple stakeholders by seamlessly sharing assets, documents, modelling resources, etc. Easily understand each operation through comprehensive context-aware help options available at every step. Develop a long-term enterprise-wide knowledge repository for artificial intelligence models - Research and Collaboration Hub
• **Automate: **Automate the end-to-end recurring processes of feature engineering, machine learning model development, and model selection by using in-built capabilities. Use automated data science to achieve the best possible model performance and improve it further with automated hyperparameter tuning. Leverage the one-click option to deploy the model once it’s developed and evaluated - Automated Data Science

Features

• **Automated Data Science: **Optimize your machine learning (ML) model development for high performance and accuracy
• **Data Fusion Hub: **Leverage an intuitive visual interface to easily prepare data at a massive scale
• **Data Visualization: **Leverage a visual interface for data exploration, comprehensive reporting, and data cleansing
• **Deployment and Monitoring Server: **Rely on the server for intuitive model deployment and performance monitoring
• **Model Development Studio: **Leverage an intuitive drag-and-drop design studio for model development
• **Research and Collaboration Hub: **Foster enterprise-wide collaboration among multiple stakeholders by seamlessly for sharing AI models