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NannyML
Building an OSS library to estimate model performance in the absence of ground truth
Monitoring
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3 Common Causes of ML Model Failure in Production
3 Common Causes of ML Model Failure in Production
Getting an ML model into production is a hard nut to crack. According to Chris Chapo, SVP of Data and Analytics at Gap, 87% of the models…
Maciej Balawejder
Jan 23, 2023
Failure Is Not an Option: How to Prevent Your ML Model From Degradation — NannyML
Failure Is Not an Option: How to Prevent Your ML Model From Degradation — NannyML
Reliance on Machine Learning models is growing. Business decisions in banking, insurance, or real-estate organizations are driven by…
Maciej Balawejder
Jan 12, 2023
Three things I learned whilst containerizing a Python API
Three things I learned whilst containerizing a Python API
I’ve been containerizing applications in all kinds of languages and frameworks for a couple of years now. Whilst I’m certainly not a guru…
Niels Nuyttens
Jun 9, 2021
Automation vs prediction in AI: how do they differ?
Automation vs prediction in AI: how do they differ?
If you deal with Machine Learning models, either as a data scientist or department manager, one of your major concerns is probably ROI. For…
Wojtek Kuberski
May 4, 2021
AI-powered underwriting can ruin your risk profile unnoticed — here is why
AI-powered underwriting can ruin your risk profile unnoticed — here is why
Progress in AI is astoundingly fast. What was a research breakthrough 5 years ago is a well-established industry standard today. Properly…
Wojtek Kuberski
Mar 22, 2021
Monitoring as a first step to observability
Monitoring as a first step to observability
Imagine you’re hiring a new employee. You put in the effort, time and money to find the right candidate, test them, onboard and…
Wojtek Kuberski
Mar 11, 2021
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