Hotwiring Data Science: AIOps and the Evolution of DevOps for Big Data

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In today’s organizations, CxOs are challenged to realize the value of artificial intelligence (AI), as well as to achieve a positive return on investments made in AI and an AI approach known as machine learning (ML). Business sponsors are struggling to reimagine their applications and services in an AI/ML world. At the same time, product development teams are finding themselves ill-equipped to consume and integrate the new models into existing applications, while data scientists, constrained by unsuitable infrastructures, are spending more time sorting out the data infrastructure than writing new models and delivering value back to their organizations.

As a result, no matter who you talk to in the enterprise about data science, AI and ML, you are likely to receive an overly optimistic response that masks some degree of frustration and disillusionment caused by missed expectations, high costs and contentious politics. It is nearly a perfect storm and is reflective of market maturity and enterprise readiness for AI/ML initiative. But there is hope, namely AI for IT operations (AIOps).

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