Search Results
9 results for “production”
A/B Testing (ML)
A/B testing in machine learning is a controlled experiment method that compares two or more model variants in production to determine which delivers superior performance on real-world business metrics.
Arize AI
Arize AI is an American ML observability and LLM evaluation platform that helps teams monitor, debug, and improve artificial intelligence models in production, offering both open-source and enterprise-grade tooling.
Canary Deployment
Canary deployment is a progressive model release strategy in which a new version is exposed to a small subset of production traffic, allowing teams to validate performance and catch failures before a full rollout.
Comet ML
Comet ML is a cloud-based MLOps platform for tracking machine learning experiments, managing model versions, monitoring production models, and evaluating large language model applications.
Helicone
Helicone is an open-source LLM observability and gateway platform that enables developers to monitor, debug, and optimise large language model applications in production with minimal integration effort.
LangSmith
LangSmith is an observability, tracing, and evaluation platform from LangChain for debugging, monitoring, and continuously improving large language model and AI agent applications in production.
MLOps
A set of practices and tools that combine machine learning, DevOps, and data engineering to automate and operationalise the full lifecycle of ML models from development through production deployment and monitoring.
Model Serving
Model serving is the discipline of deploying trained machine learning models behind APIs or runtimes so that production applications can request predictions at scale with predictable latency, throughput, and reliability.
Shadow Mode
Shadow mode is a machine learning deployment strategy in which a new model processes live production traffic in parallel with the existing model, capturing outputs for evaluation without affecting users or business operations.