MLOps and AI Observability platform for deploying, serving, observing AI models
Streamlines AI model deployment and observability
Real-time data exploration, drift detection, model monitoring
Supports on-premises and SaaS deployment options
Pricing:
Features:
Categories:
#Development & CodeRadicalbit ai is an MLOps and AI Observability platform designed to streamline the deployment, serving, observability, and explainability of AI models. It enables data teams to maintain control over the entire data lifecycle through real-time data exploration, outlier and drift detection, and model monitoring in production. The platform supports seamless integration into existing ML stacks, whether via SaaS or on-premises, and enhances compliance with regulatory standards such as the European Union AI Act.
- Data Transformation: Design and run real-time data transformation pipelines using a visual canvas with prebuilt operators or custom Python code.
- Data Integrity: Ensure data integrity by mitigating data and concept drift. Identify missing values and outliers, and manage ranges and schema evolution.
- Score Predictions: Run model inference via pipelines or APIs, securely storing both online and offline features and predictions within a built-in feature store.
- Monitor & Observe: Track model activity and performance for Machine Learning, Computer Vision, and LLMs. Enable Continual Learning by auto-triggering retraining when performance declines.
- Explain Behavior: Understand AI model outputs clearly to avoid bias, achieve compliance, and optimize business processes.
- Create & Monitor RAG Apps: Combine LLMs with your knowledge bases by developing and monitoring custom RAG applications with Radicalbit.
- Simpler, Faster, Better MLOps: Radicalbit supercharges AI model deployment, serving, observability, and explainability, helping data teams maintain full control over the data lifecycle.
- 92% Faster Time-to-Value: Achieve an average 92% reduction in time-to-value when deploying ML pipelines to AI-powered applications.
- Cost Reduction: Save time and avoid obsolescence through automations, outlier and drift detection, and metric monitoring.
- Scalability & Sustainability: Adjust workloads and save energy with scale-to-zero and automated resource management.
- Control & Governance: Identify potential issues and risks timely using advanced monitoring and observability. Explain models and achieve fairness.
- Seamless Integration: Deploy Radicalbit platform as SaaS or on-prem, on private cloud, or your own infrastructure.
- Plug & Play: Easily integrate Radicalbit into your AI stack, working with self-trained MLflow models or importing them directly from Hugging Face.
- Low-Code & APIs: Access Radicalbit’s features via an intuitive visual UI and APIs, supporting industry-standard languages like Python, Java, and JavaScript.
Radicalbit ai
MLOps and AI Observability platform for deploying, serving, observing AI models
Key Features
Links
Visit Radicalbit aiProduct Embed
Subscribe to our Newsletter
Get the latest updates directly to your inbox.