Hive AutoML is a cloud-based automated machine learning (AutoML) platform that enables users to build, fine-tune, and deploy custom machine learning models without writing code. The platform supports both Hive’s proprietary models and popular open-source models, covering use cases such as image and text classification, sentiment analysis, moderation, and chat. Users can easily manage structured and unstructured datasets, automate data labeling, and set up advanced data workflows like embedding pipelines for retrieval-augmented generation (RAG). Hive AutoML provides default training options and allows customization of hyperparameters, with real-time metrics such as balanced accuracy, precision, and recall available during and after training. Once models are trained and evaluated, they can be deployed for inference and integrated into production workflows or Hive’s Moderation Dashboard. The platform is designed for rapid prototyping, scalable deployment, and seamless integration into enterprise applications.

Key Features

No-Code Model Building Create and deploy custom ML models without programming.

• Intuitive interface for model selection and training
• Supports image and text classification, sentiment analysis, moderation, and chat

Dataset Management Flexible tools for handling structured and unstructured data.

• Upload, label, and manage datasets in popular formats
• Automate data labeling and workflow setup

Custom Model Fine-Tuning Adapt models to specific business needs.

• Fine-tune proprietary and open-source models
• Customize hyperparameters and training configurations

Performance Evaluation Monitor and assess model quality.

• Real-time metrics: balanced accuracy, precision, recall
• Evaluation during and after training

Seamless Deployment Integrate models into production workflows.

• One-click deployment to Hive Models for inference
• Integration with Moderation Dashboard and APIs

Support for Advanced Workflows Enable complex ML scenarios.

• Embedding pipelines for retrieval-augmented generation (RAG)
• Automation of key data workflows

Benefits

Rapid Prototyping Accelerates development and testing of ML solutions.

• Build and deploy models in minutes
• Experiment with different configurations easily

Scalability and Flexibility Adapts to diverse enterprise needs.

• Supports multiple model types and deployment options
• Handles large-scale data and inference workloads

Accessibility Empowers non-technical users to leverage ML.

• No coding required
• Intuitive, user-friendly interface

Integration and Automation Streamlines ML operations.

• Connects with existing production workflows
• Automates data preparation and model deployment