#MLOps

Mariya Davydova

neuromasha

Funny fact: when I was a software developer, I thought that #DevOps is a challenging practice; now I develop and advocate #MLOps and sometimes feel that it would be so

Shreya Shankar

sh_reya

I have been feeling tired lately when thinking about the differences between MLOps and DevOps. There are so many “gotchas” to keep track of in production ML systems, but I

Krishna Gade

krishnagade

I was an eng leader on Facebook’s NewsFeed and my team was responsible for the feed ranking platform. Every few days an engineer would get paged that a metric e.g.,

Chip Huyen

chipro

When talking to people who haven’t deployed ML models, I keep hearing a lot of misperceptions about ML models in production. Here are a few of them.(1/6) 1. Deploying ML

Shreya Shankar

sh_reya

My thoughts on baselines, a concept that is *extremely* relevant in industry ML but does not exactly translate from academic ML: 1/9 In academic ML projects, my classmates and I

Gabriel Bianconi

GabrielBianconi

My skillset when I started consulting in 2019 was primarily deep learning for vision and NLP.In 2020, it expanded: neural search; recommendations; predictive analytics beyond deep learning; and ML for

Thomas G. Dietterich

tdietterich

A review of “Underspecification Presents Challenges for Credibility in Modern Machine Learning” by D’Amour et al.https://arxiv.org/abs/2011.03395 0/ Main claim: ML pipelines exhibit “underspecification”. i.e., they can produ

Shreya Shankar

sh_reya

Unit testing for ML pipelines is challenging given changing data, features, models, etc. Changing I/O make it hard to have fixed unit tests. To hackily get around this, I liberally

Krishna Gade

krishnagade

COVID-19, and the subsequent economic effects it triggered, is a major factor behind widespread MLmodel degradation across all kinds of models in the industry today. Here is how MLOps Monitoring

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