What is Radicalbit?
Radicalbit is an MLOps and AI Observability platform designed to streamline the deployment, serving, observability, and explainability of AI models, including Machine Learning, Computer Vision, and LLMs. The platform helps data teams maintain full control over the entire data lifecycle, with features for real-time data exploration, identification of outliers and drift, and comprehensive model monitoring in production environments.
Radicalbit offers seamless integration, with options for SaaS or on-premise deployment. Users can easily integrate it within their existing ML stack, work with self-trained MLflow models, or import models from Hugging Face. The platform can be accessed via a user-friendly visual UI and APIs, supporting popular languages such as Python, Java, and JavaScript.
Features
- AI Model Deployment & Serving: Upload MLflow models or import from Hugging Face using the UI or APIs.
- Data Transformation: Design and run real-time data transformation pipelines with prebuilt operators or custom Python code.
- Data Integrity: Ensure data integrity, mitigate data & concept drift, identify missing values and outliers, and manage schema evolution.
- Score Predictions: Run model inference via pipelines or APIs, securely storing online and offline features and predictions.
- AI Observability: Track model activity and performance for Machine Learning, Computer Vision, and LLMs, with auto-triggering for retraining.
- Explainability: Understand the output of AI models to avoid bias, achieve compliance and optimize processes.
- RAG Applications: Develop and monitor custom RAG applications, combining LLMs with your knowledge bases.
Use Cases
- Deploying and serving machine learning models at scale.
- Monitoring and observing AI model performance in real-time.
- Ensuring data integrity and mitigating data drift.
- Explaining AI model behavior for compliance and optimization.
- Creating and managing RAG applications.
- Stream processing and data transformation.
- Automating retraining of models based on performance decline.
- Managing the whole lifecycle of AI Models.
