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The past few years in AI have been marked by the great migration from on-premises data centers to public cloud infrastructure. Organizations are drawn to the cloud’s promise of pay-as-you-go, usage-based pricing and capacity easy to scale up and down based on needs; however, in practice, leveraging big data with good performance and at reasonable costs is easier said than done.
In reality, processing large amounts of data in pipelines that deliver real business value via data science, machine learning, and AI projects requires not only serious computational power, but also optimized resource consumption and isolated environments for development and production. On top of all of this, businesses need to put best practices in place that drive efficiency and cost monitoring — clearly, managing all of these moving parts can get complex quickly for organizations of any size, digital native or not.