Amazon Lightsail
The fastest path from AI concept to production with predictable cloud infrastructure.

Open-source MLOps platform for automated model serving, monitoring, and explainability in production.
Hydrosphere is a comprehensive open-source MLOps ecosystem designed to bridge the gap between model development and production-grade deployment. Its architecture is built around a Kubernetes-native design, allowing for seamless scaling of model serving via gRPC and REST interfaces. In the 2026 market landscape, Hydrosphere differentiates itself by providing deep integration between model monitoring and explainability, enabling teams to not only detect performance degradation but also diagnose the underlying statistical drivers using integrated SHAP and LIME algorithms. The platform manages the entire lifecycle of a model version, providing immutable deployments that ensure reproducibility. Its monitoring suite specifically targets data drift, model latency, and accuracy metrics, triggering automated alerts or rollbacks when thresholds are violated. Hydrosphere's 'Manager' component acts as the central brain for versioning and metadata management, while 'Serving' nodes handle high-throughput inference tasks. It is particularly valued by organizations requiring high-security, self-hosted environments where data privacy is paramount, offering a robust alternative to SaaS-only observability platforms.
Enables mirroring of production traffic to a new model version without affecting the end-user response.
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.
Statistical comparison of production input data against a defined training baseline using Kolmogorov-Smirnov and Chi-squared tests.
Native implementation of SHAP and LIME to provide local and global explanations for individual predictions.
Immutable versioning system that packages model binaries, environments, and metadata together.
Hooks that capture a configurable percentage of inference traffic for asynchronous analysis.
Pre-built runtimes for TensorFlow, Scikit-learn, ONNX, and PyTorch, with custom runtime support via Docker.
Visual interface to correlate drift in specific features with overall model performance drops.
Model performance degrades as fraud patterns evolve (concept drift).
Registry Updated:2/7/2026
Retrain and deploy updated version.
Difficulty in comparing multiple model versions in a live environment.
Need for interpretability in medical AI to comply with regulations.