2 citations · 3 across the 6 of their papers we have counts for
3 papers · 1 filter
Data Drift Monitoring for Log Anomaly Detection Pipelines
Dipak Wani, Samuel Ackerman, Eitan Farchi +3
Logs enable the monitoring of infrastructure status and the performance of associated applications. Logs are also invaluable for diagnosing the root causes of any problems that may…
Automatic Generation of Attention Rules For Containment of Machine Learning Model Errors
Samuel Ackerman, Axel Bendavid, Eitan Farchi +1
Machine learning (ML) solutions are prevalent in many applications. However, many challenges exist in making these solutions business-grade. For instance, maintaining the error rat…
Theory and Practice of Quality Assurance for Machine Learning Systems An Experiment Driven Approach
Samuel Ackerman, Guy Barash, Eitan Farchi +2
The crafting of machine learning (ML) based systems requires statistical control throughout its life cycle. Careful quantification of business requirements and identification of ke…