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Proof-of-concepts don’t drive transformation—production-grade AI does. Most enterprises struggle to operationalize models due to fragmented tooling, poor governance, and slow iteration cycles.
CNET Global’s AI/ML at Scale solutions combine automated MLOps pipelines, end-to-end model lifecycle management, and domain-specific accelerators to move your AI from lab to real-world results—fast.
Reduce time from prototype to production by 70%
Track lineage, performance, and compliance
Deliver use cases that drive real business outcomes
Modular, reusable, and scalable model architectures
1. Version control, experiment tracking, CI/CD for models .
2. Performance monitoring and automated drift detection
1. Infrastructure-as-code, GitOps, and containerized deployments.
2. End-to-end orchestration from data prep to retraining.
1. Real-time data pipelines using Kafka, Spark, and Kinesis.
2. Feature stores for cross-team reuse and governance.
Real-time anomaly detection with graph-based and supervised learning models.
Sensor data analytics to forecast equipment failure and reduce downtime
Patient data models for early diagnosis and intervention
Deep learning-based forecasting for inventory, pricing, and staffing
Data scientists, engineers, and domain experts
Kubeflow, MLflow, SageMaker, Airflow
Use-case-first, not model-first
De-risked, faster time-to-value
Get a free readiness audit tailored to your business priorities.
