AI Platform
Enterprise AI Platform. An enterprise AI platform that integrates development, deployment, and operations management of AI models based on MLOps. Supports End-to-End from data preparation to model deployment.
MLOps
End-to-End MLOps Platform
Build an MLOps environment that manages the entire lifecycle of AI models from development to deployment and operations. Provides a secure machine learning operations (MLOps) environment for enterprises to independently operate AI.
Addresses initial investment burden, uncertainty about implementation effectiveness, and data integration limitations with existing equipment, providing customized solutions that understand industry-specific characteristics.
Features
Key Features
Data Pipeline
Builds automated pipelines for data collection, preprocessing, and feature engineering. Efficiently processes large-scale data and transforms it into formats required for model training using Apache Spark and Airflow.
Model Training
Provides distributed training environments utilizing GPU/TPU clusters. Efficiently discovers optimal models through automated hyperparameter tuning (AutoML) and experiment tracking.
Model Registry
Systematically tracks model version management and metadata. Consistently manages model performance metrics, training parameters, and dataset information through an MLflow-based registry.
Real-time Inference
Provides high-performance real-time inference at millisecond latency with Triton Inference Server-based GPU acceleration. Flexibly responds to traffic fluctuations with auto-scaling and load balancing.
A/B Testing
Compares the performance of multiple models in real time and gradually rolls them out. Safely deploys new models to production with canary and blue/green deployment strategies.
Monitoring
Automatically detects model drift and data drift and sends alerts. Monitors key metrics such as inference latency, error rate, and accuracy on a real-time dashboard.
Technology
Technology Stack
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