Model Development Studio

Leverage an intuitive drag-and-drop design studio for model development

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 Model Training - Model Development Studio

Model Training

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

Model Development Studio

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
Model Development Studio

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

Capabilities of Newgen AI Cloud

Make informed business decisions and deliver a transformed customer experience

Visually prepare data at a massive scale

Leverage an intuitive drag-and-drop design studio for model development

Rely on the server for intuitive model deployment and performance monitoring

Optimize your machine learning (ML) model development for high performance and accuracy

Leverage a visual interface for data exploration, comprehensive reporting, and data cleansing

Foster a conducive environment for better collaboration and research