Archives for Technical debt
“Model drifts are more common when models in high-stakes domains, such as air quality sensing or ultrasound scanning due to lack of curated datasets.” When AI models are applied in high-stakes domains like health and industrial automation, data quality suddenly becomes a significant aspect of the whole pipeline. Models in the real world are prone…
The post It’s High Time ML Community Looked Into Effects of Data Cascades appeared first on Analytics India Magazine.
It is humbling to think of the number of tools, languages, techniques and applications a machine learning ecosystem has nurtured. Choosing the best fit out of these hundreds of options and then bringing them together to work seamlessly is a data scientist’s nightmare. The hidden technical debts in a machine learning (ML) pipeline can incur […]
The post How To Handle Hidden Technical Debt In A Machine Learning Pipeline appeared first on Analytics India Magazine.
If you are from the software industry, there are a good number of chances that you have come across the term “Technical Debt” at least once. But what exactly is technical debt? Technical debt a.k.a tech debt or code debt is a metaphor coined by software developer Ward Cunningham. This term is used to describe…
The post 5 Ways Companies Can Prevent Technical Debt In Open Source Projects appeared first on Analytics India Magazine.