We’re excited to release into beta v1.0 of Elemeta, our open-source library for exploring, monitoring, and extracting features from unstructured data.
End-to-end ML observability
Everything you need to keep your ML healthy in production.
Put everything in context
Monitoring incidents
Resolve problems quickly with incident investigations that correlate and group anomalies to deliver a big-picture overview of why models are misbehaving. Giving you everything you need to quickly pinpoint casualty and resolve issues before they impact your business.
Take action at scale
Cross-model projects
Share segments, manage configurations, and monitor models in a single project flow and gain observability into cross-pipeline macro-events such as missing values, performance decay for a specific segment across all models, and so forth.
Tap into production insights
Model analytics
Instantly see your model’s behavioral profile and drill down into any granular detail. Analyze and compare different datasets and production timeframes to detect changes.
Retrain on what really matters
Data similarity matrix
Optimize your models with production observability insights to identify retraining opportunities and strategies and underperforming segments.
See what Superwise can do for you
Try Superwise out or contact us to learn more.
Featured resources
Building your MLOps roadmap
Scaling up your model operations? in this blog we will offer some practical advice on how to build your MLOps roadmap
Build or buy? Choosing the right strategy for your model observability
If you’re using machine learning and AI as part of your business, you need a tool that will give you visibility into the models that are in production: How is their performance? What data are they getting? Are they behaving as expected? Is there bias? Is there data drift? Clearly, you can’t do machine learning
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