ML Production Investigation Room

Root Cause Analysis

Get to the root cause quick, gain actionable insights, and explore your production data in a live, collaborative, notebook experience.

Production ML is easy, ‘till it breaks

Deploying ML models to production can seem like an easy task until issues arise. 
Most often, over 90% of these issues stem from data pipeline disruptions. When this happens, pinpointing the problematic feature becomes crucial. Root cause analysis helps identify and fix these challenges swiftly.

Troubleshoot ML 
models with ease

No need to export to external notebook

  • Get quick and easy access to your production data within the same alerting platform.
  • No exporting and mapping, just explore your data.
  • Gain insights, discover patterns, and find the root cause of triggered alerts.

Segment, drift, and distribution analysis

Save time investigating production data

  • Optimize production models through deep data segmentation, pinpointing drift origin, and understanding data distributions.
  • Enhance model performance and reveal new opportunities for optimization.

Explore embeddings in unstructured data

Unstructured data doesn’t have to be a pain

  • Effectively visualize embeddings in unstructured data through 2D/3D projections using UMAP.
  • Pinpoint different data clusters and analyze patterns.
  • Explore and investigate the performance of NLP, LLMs, and CV models with precision.
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“In a space that is developing fast and offerings multiple competing solutions, Aporia’s platform is full of great features and they consistently adopt sensible, intuitive approaches to managing the variety of models, datasets and deployment workflows that characterize most ML projects. They actively seek feedback and are quick to implement solutions to address pain points and meet needs as they arise.”

Felix D.

Principal, MLOps & Data Engineering

“As a company with AI at its core, we take our models in production seriously. Aporia allows us to gain full visibility into our models' performance and take full control of it."

Orr Shilon

ML Engineering Team Lead

“ML models are sensitive when it comes to application production data. This unique quality of AI necessitates a dedicated monitoring system to ensure their reliability. I anticipate that similar to application production workloads, monitoring ML models will – and should – become an industry standard.”

Aviram Cohen

VP R&D

“With Aporia's customizable ML monitoring, data science teams can easily build ML monitoring that fits their unique models and use cases. This is key to ensuring models are benefiting their organizations as intended. This truly is the next generation of MLOps observability.”

Guy Fighel

General Manager AIOps

“ML predictions are becoming more and more critical in the business flow. While training and benchmarking are fairly standardized, real-time production monitoring is still a visibility black hole. Monitoring ML models is as essential as monitoring your server’s response time. Aporia tackles this challenge head on.”

Daniel Sirota

Co-Founder | VP R&D

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