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The adoption of AI/ML in financial services is increasing as companies seek to drive more robust, data-driven decision processes as part of their digital transformation journey. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. But productionising machine learning at scale is challenging.
Toil. Few other words have the same visceral impact for SREs as their four-letter nemesis: toil. Although pretty much everyone recognizes and agrees that toil is bad, it is a term that is frequently misused in colloquial use. In common English usage, toil is defined as “long strenuous fatiguing labor”. As a term of art in the SRE profession, “toil” has several very specific characteristics which distinguish it from other sorts of work which people spend time on.
How can enterprises streamline their operations for improved internal efficiency and customer satisfaction? The right combination of DevOps tools will do just that.
Companies face multiple challenges when migrating their applications and services to the cloud, and one of them is infrastructure management. The ideal scenario would be that all workloads could be containerized. In that case, the organization could use a Kubernetes-based service, like Amazon Web Services (AWS), Google Cloud or Azure, to deploy and manage applications, services and storage in a cloud native environment. Unfortunately, this scenario isn’t always possible.