Systems for Machine Learning

On the field of Machine Learning Systems and how it addresses the new challenges of ML with a lens shaped by traditional systems research

Systems for Machine Learning

Over the past decade, machine learning (ML) has become a critical component of countless applications and services in a variety of domains. Fields ranging from healthcare to autonomous vehicles have been transformed by the use of ML techniques.

Machine learning’s increasing importance to real-world applications brought awareness of a new field focused on ML in practice - machine learning systems (or, as some call it, MLOps). This field acts as a bridging point between the domains of computer systems and machine learning, considering the new challenges of machine learning with a lens shaped by traditional systems research.

So what are these “ML challenges”?

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