Model Development Studio

Streamline the end-to-end data science lifecycle by leveraging an intuitive drag-and-drop interface for rapid model development, experimentation, and evolution. Leverage the in-built modeling algorithms to develop and deploy models on extensive datasets. Perform detailed evaluation of models through visual performance metric reports, thereby identifying, training, and optimizing for the best-fit model

Features

• Profile Your Data for Completion, Accuracy, and Validity - Perform data profiling operations on structured and unstructured data, such as one-hot encoding, stemming, lemmatization, missing value imputation, and count vectorizer, etc. - Use built-in machine learning (ML) and deep learning-based techniques for dimensionality reduction, including singular value decomposition (SVD), principal component analysis (PCA), and restricted Boltzmann machine (RBM)
• Utilize Rich Modeling Algorithms and Techniques - Use multiple options to model, including graph, ML, deep learning, and natural language processing - Perform model averaging techniques—stacking and ensembling - Develop models on massive-scale datasets by utilizing the in-memory distributed computing-based processing
• Design the Model Pipeline Visually - Perform rapid model experimentation, development, and evolution through the visually intuitive drag-and-drop interface - Configure each node and drop it on the canvas with others to build your own model pipeline
• Engineer Features for Supervised and Unsupervised Learning - Create and define your own features, based on separate boolean and aggregate operations with comprehensive feature engineering - Use the coding interface or the visual workflow editor to create new data columns
• Access In-built Segmentation Operations - Create segments on both numeric and textual data - Create user-defined rules and conditions for segment creation - Make use of both macro and micro-level segmentation
• Evaluate Models in Detail - Select the best models based on several visual performance metric reports - Evaluate the model performance using the rich set of evaluation metrics - Use multiple modeling techniques on the same feature engineered data with multi-model experimentation and evaluation
• Leverage AI as a Glass Box - Access and configure all the modeling parameters - Fetch a detailed ‘feature importance report,’ explaining the output