Revolutionizing data management and model building
Experience unmatched productivity, flexibility, and security with Refract’s cutting-edge workflow solutions and governance tools
Safeguard against breaches, simplify data extraction, prep your data effortlessly with 100+ functions, and register models seamlessly with Refract ML SDK. Experience the productivity boost with Refract Wave – your one-stop shop for interactive data exploration and model building.
From multiple languages and IDEs to container sizes tailored for your workload, cross-platform compatibility, and seamless Git integration, users can experience unmatched flexibility.
Audit logs, RBAC, version control, and a model catalog enhance the modeling game and ensure governance.
How it works
Explore, clean, build, and evaluate ML models seamlessly with the Build Model capability. It streamlines the entire process, from identifying business use cases and exploring datasets to running feature engineering jobs, provisioning data, and tracking machine learning models. With support for libraries like sci-kit and TensorFlow, performance evaluation, stakeholder communication, and seamless model registration, it’s a comprehensive toolkit for turning data into impactful insights.
Additional capabilities of the Insight Designer module
Make your models production-ready with unparalleled ease and efficiency. Streamline workflows, optimize resource allocation, and empower your team for innovation, ensuring every resource is maximized for peak performance.
Ensure peak performance, robust generalization, and ethical AI practices. Uncover insights, mitigate risks, and maximize accuracy through rigorous testing, validation, and continuous monitoring. Trust your ML models to make informed decisions.
With real-time model monitoring, gain optimal performance, detect anomalies, and adapt to changing data seamlessly. With our robust and automated monitoring framework, stay confident in AI-driven decisions, ensuring sustained success for your business.
Experience robust model governance and ensure ethical AI and regulatory compliance. From risk mitigation to policy enforcement and accountability, elevate trust and efficiency across the entire machine learning lifecycle for sustained success.