ARTIFACT DESIGN

Artifact Design and Pipeline in MLOps Part I

In MLOps, most of the work focuses on the inference stage, specifically the development of microservices. However, the broader picture goes beyond this—it includes aspects ranging from programming practices to resource utilization that need to be evaluated. This is where the role of a Machine Learning DevOps Engineer becomes crucial. In this post, I want to address this profile by approaching it from the perspective of designing a model.

EDGE COMPUTING AND EDGE MACHINE LEARNING

Introduction

Data scientists often face three possibilities when deploying our products into production: through the cloud, edge computing, or the more recent variant, Edge Machine Learning. To introduce these concepts and provide context for this post, I will start by discussing the foundation—AI Computing.