Cloud Computing

Abstract

IT infrastructure landscape has been fundamentally reshaped by cloud computing, giving businesses around the world scalable, flexible and cost-effective options for all of their needs. This chapter examines how cloud computing evolved into a backbone for hosting data analytics platforms and machine learning (ML) workloads. We analyse the architecture of cloud computing with a major focus on software and hardware components that allow proper processing of large datasets and training of very complex ML models. Factors to consider in transitioning to the cloud-based ML workflows include scalability, cost optimization and existing system integration. Beyond that, we point out how cloud computing enables faster innovation, agility and global collaboration in ML and data-driven decision-making. The discussion also briefly covers the new developments of serverless ML, hybrid cloud models and edge-to-cloud integration and their implications on data analytics and ML workflows. Emerging trends such as serverless ML, hybrid cloud models and edge-to-cloud integration are discussed in the context of their impact on data analytics and ML workflows.

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