Amazon Lightsail
The fastest path from AI concept to production with predictable cloud infrastructure.
The Enterprise ModelOps Platform for Scalable, Secure, and Explainable AI Deployment at the Edge and Cloud.
Modzy is a leading ModelOps and Edge AI platform designed to accelerate the deployment, management, and governance of AI models across enterprise environments. Built with a microservices-based architecture on Kubernetes, Modzy addresses the 'last mile' of AI by providing a standardized framework for containerizing models using OCI-compliant standards. As of 2026, Modzy has solidified its position in the market by bridging the gap between data science teams and IT operations, offering seamless integration with hybrid-cloud and disconnected edge environments. Its technical core features a high-performance inference engine, automated scaling, and a centralized model marketplace that supports internal and third-party models. Unique to Modzy is its focus on AI security and ethics, incorporating built-in adversarial attack detection and 'Explainable AI' (XAI) features that provide transparency into model decision-making processes. This makes it a preferred choice for regulated industries such as defense, healthcare, and finance. The platform's ability to run models in resource-constrained edge devices while maintaining centralized governance allows organizations to process data locally and act in real-time without the latency of cloud-only solutions.
Integrated LIME and SHAP algorithms to provide feature importance visualizations for every prediction.
The fastest path from AI concept to production with predictable cloud infrastructure.
The open-source multi-modal data labeling platform for high-performance AI training and RLHF.
Scalable, Kubernetes-native Hyperparameter Tuning and Neural Architecture Search for production-grade ML.
The enterprise-grade MLOps platform for automating the deployment, management, and scaling of machine learning models.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses statistical monitoring to detect potential model poisoning or evasion attacks in real-time.
A lightweight runtime designed for ARM and x86 architectures to run models in disconnected or low-bandwidth environments.
Full lifecycle management with zero-downtime hot-swapping of model versions via Kubernetes sidecars.
An internal app-store like environment for sharing models across different business units with audit trails.
Automated detection of data drift and concept drift with configurable alerting thresholds.
Strict logical isolation of data and models within a single infrastructure footprint.
Latency and data privacy concerns prevent sending high-res camera feeds of production lines to the cloud.
Registry Updated:2/7/2026
Sync metadata to cloud for weekly reporting.
Processing massive geospatial datasets quickly to identify building damage after a disaster.
Maintaining high throughput for transaction monitoring while ensuring auditability for regulators.