Deep Learning at Scale

Preface

I started my professional career as a software engineer. Over the course of my time in that role, I became deeply interested and involved in running software and systems at scale. I learned a lot about distributed systems, performance, optimizations, and running them reliably at scale. Subsequently, I went on to perform many other roles, from building systems at the intersection of software and operations (DevOps) and auxiliary systems to enable intelligent software (MLOps), to running deep learning inference at scale and developing data engines for deep learning (machine learning engineering), to developing multitasking, multiobjective models for critical functions such as healthcare and business decision workflows as a data scientist and machine learning specialist.

Since I’ve become involved in building intelligent systems, deep learning is a big part of what I do today. The wide adoption of deep learning–based intelligent (AI) systems is motivated by its ability to solve problems at scale with efficiency. However, building such systems is complex, because deep learning is not just about algorithms and mathematics. Much of the complexity lies at the intersection of hardware, software, data, and deep learning (the algorithms and techniques, specifically). I consider myself fortunate to have gained experience in a series of roles that forced me to rapidly develop a detailed understanding of building and managing deep learning–based AI systems at scale. The knowledge that ...