Visual machine learning to accelerate productivity
Drag-and-drop interface to speed up model building and deployment for the entire data science team, from beginners to professionals.
Connect to any data source and prepare and preprocess data using a variety of built-in modules
Build and train models visually using the latest machine learning and deep learning algorithms
Use drag and drop modules to validate and evaluate models
Deploy and publish real-time or batch inference endpoints with a few clicks
Connect and prepare data with ease
Drag and drop a registered dataset, connect to various data sources including Azure blob, Azure Data Lake, Azure SQL or upload from a local file. One-click to preview and visualize the data profile. Preprocess data using a rich set of built-in modules for data transformation and feature engineering.
Build and train models without writing code
Build and train machine learning models with state-of-the art machine learning and deep learning algorithms, including those for computer vision, text analytics, recommendation and anomaly detection. Drag and drop modules for no-code models or customize using Python and R code.
Validate and evaluate model performance
Interactively run machine learning pipelines.
Cross-validate models and datasets for accuracy.
Access data visualizations to evaluate models with a few clicks. Perform quick root cause analysis using the graphs, preview logs and outputs for debugging and troubleshooting.
Deploy models and publish endpoints with a few clicks
Deploy models for real-time and batch inferencing as REST endpoints to your environment, with a few clicks. Automatically generate scoring files and the deployment image. Models and other assets are stored in the central registry for MLOps tracking and lineage.