Machine Learning Operations: Latent Conditions and Active Failures
Machine Learning Operations: Latent Conditions and Active Failures


Machine Learning Systems play a huge role in several businesses from the Banking industry to recommender systems in entertainment applications until health domains. The era of ""A Data Scientist with a Script in a single machine"" is officially over in high stakes ML.

We're entering an era of Machine Learning Operations (MLOps) where those critical applications that impact society and businesses need to be aware of aspects like active failures and latent conditions. This talk will discuss risk assessment in ML Systems from the perspective of reliability, safety, and especially causal aspects that can lead to the rise of silent risks in said systems.


Flavio works with information technology, he has acquired experience in some companies in different businesses. Most of these roles were performed in parallel or throughout my overall experience. Currently works as head and machine learning engineer in a chapter of core machine learning, embedded algorithms at telecommunication business platforms for mobile products.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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