Orchestrate your entire ML lifecycle with a single, customizable platform
Whether you’re an experienced data scientist or new to machine learning, effortlessly create models and convert them into training pipelines, with or without coding. Explore diverse environments of your choice on our unified platform, enabling seamless ML lifecycle management. Easily review your experiment history in one comprehensive view, using both internal and external repositories, to streamline iterative cycles and consistently enhance model performance.
Ensure model reproducibility and operational stability with seamless DevOps integration
Guarantee model reproducibility and traceability by effortlessly cloning your development environment to production. Retrain and redeploy models with ease using API, batch, or real-time serving—all without the need for a dedicated data scientist. This allows for rapid scaling of AI service operations and ensures operational stability by monitoring inference performance in real-time and tracking inference logs.
Optimize compute resource management with a robust governance framework
Easily assign and manage resources for projects and tasks, optimizing compute resource usage through autoscaling. Ensure AI service security with user authority management features and seamless integrations with your existing infrastructure—whether on-premises, in the public or private cloud, or in a hybrid environment—without requiring major changes.
Enhance dataset traceability with fast connection and convenient feature engineering
Connect to various data sources, seamlessly combine and generate datasets, all with just a few clicks. Foster collaboration among project members on the same dataset, enhancing traceability in both data and model development.
Effortlessly create your dataset by easily connecting data from diverse sources with just a few clicks.
Model Development
Accelerate the development of models with our intuitive ML development environment and streamline pipeline creation.
Model Deployment
Seamlessly deploy your trained models and adapt them to your environment by selecting from a range of deployment methods, including APIs or streams.
Model Retraining and Monitoring
Effortlessly retrain, debug, and redeploy models using pre-existing pipelines, saving time and enabling focused optimization. Monitor model status and gain valuable insights with our intuitive user interface.
Data Preparation
Model Development
Model Deployment
Model Retraining and Monitoring
Data Preparation
Model Development
Model Deployment
Model Retraining and Monitoring
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