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Building your AI data pipeline
Artificial intelligence, the erstwhile fascination of sci-fi aficionados and the perennial holy grail of computer scientists, is now ubiquitous in the lexicon of business. Now more modern-business-imperative than fiction, the world is moving toward AI adoption fast. According to Forrester Research, AI adoption is ramping up. 63 percent of business technology decision makers are implementing, have implemented, or are expanding use of AI. The stakes are high. AI promises to help business accurately predict changing market dynamics, improve the quality of offerings, increase efficiency, enrich customer experiences and reduce organizational risk by making business, processes and products more intelligent. Such competitive benefits present a compelling enticement to adopt AI sooner rather than later. AI is finding its way into all manner of applications from AI-driven recommendations, to autonomous vehicles, virtual assistants, predictive analytics and products that adapt to the needs and preferences of users. But as many and varied as AI-enabled applications are, they all share an essentially common objective at their core—to ingest data from many sources and derive actionable insights or intelligence from it.
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