Arize AI
                
                Automatically discover issues, diagnose problems, and improve models with Arize’s machine learning observability platform. Machine learning systems address mission critical needs for businesses and their customers every day, yet often fail to perform in the real world. Arize is an end-to-end observability platform to accelerate detecting and resolving issues for your AI models at large. Seamlessly enable observability for any model, from any platform, in any environment. Lightweight SDKs to send training, validation, and production datasets. Link real-time or delayed ground truth to predictions. Gain foresight and confidence that your models will perform as expected once deployed. Proactively catch any performance degradation, data/prediction drift, and quality issues before they spiral. Reduce the time to resolution (MTTR) for even the most complex models with flexible, easy-to-use tools for root cause analysis.
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                Gantry
                
                Get the full picture of your model's performance. Log inputs and outputs and seamlessly enrich them with metadata and user feedback.
Figure out how your model is really working, and where you can improve. Monitor for errors and discover underperforming cohorts and use cases.
The best models are built on user data. Programmatically gather unusual or underperforming examples to retrain your model.
Stop manually reviewing thousands of outputs when changing your prompt or model. Evaluate your LLM-powered apps programmatically.
Detect and fix degradations quickly. Monitor new deployments in real-time and seamlessly edit the version of your app your users interact with.
Connect your self-hosted or third-party model and your existing data sources.
Process enterprise-scale data with our serverless streaming dataflow engine.
Gantry is SOC-2 compliant and built with enterprise-grade authentication.
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                SAM4
                
                Eliminate unplanned downtime in AC motors & rotating assets. We predict over 9 out of 10 of failures up to 5 months in advance using electrical waveform analysis. Predictive maintenance for maintenance professionals. Deploy your scarce maintenance resources to assets that actually need help. SAM4 enables maintenance teams to perform maintenance only when a developing fault is detected. Act faster using real-time fault diagnosis. Once a fault is detected, SAM4 can also identify the specific fault at play. This means the maintenance team can get right to the source of the problem without having to thoroughly inspect the entire asset. Succeed where previous pilots with predictive maintenance failed. SAM4 is up to 20% more accurate than traditional vibration-based systems, and is also faster and cheaper to install and maintain. Detect over 9 out of 10 failures  SAM4 is up to 50% more accurate than the vibration-based systems you’ve tried before. 
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                Traceloop
                
                Traceloop is a comprehensive observability platform designed to monitor, debug, and test the quality of outputs from Large Language Models (LLMs). It offers real-time alerts for unexpected output quality changes, execution tracing for every request, and the ability to gradually roll out changes to models and prompts. Developers can debug and re-run issues from production directly in their Integrated Development Environment (IDE). Traceloop integrates seamlessly with the OpenLLMetry SDK, supporting multiple programming languages including Python, JavaScript/TypeScript, Go, and Ruby. The platform provides a range of semantic, syntactic, safety, and structural metrics to assess LLM outputs, such as QA relevancy, faithfulness, text quality, grammar correctness, redundancy detection, focus assessment, text length, word count, PII detection, secret detection, toxicity detection, regex validation, SQL validation, JSON schema validation, and code validation. 
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