Cloud native is the path to cloud innovations like AI/ML

Lift and shift? Cloud benefits may be hard to come by

For a variety of reasons—technical debt, application or platform preferences, or simple familiarity—many companies hesitate to refactor certain applications to be cloud-native. Instead, they prefer a “lift and shift” strategy that ports their applications to a cloud infrastructure without optimizing the code for performance in a cloud environment. In doing so, they may miss out on some of the benefits the cloud has to offer such as increased performance, ease of maintenance, scalability, and flexibility in data access and storage. In this episode, Mike Kavis and guest, Deloitte’s Sudi Bhattacharya, discuss some common drawbacks of not going cloud-native with legacy applications and typical benefits companies can realize if they refactor at least some of their code to be more compatible with cloud-native coding practices. This wide-ranging discussion also touches on machine learning (ML) and AI, and the potential benefits of leveraging the cloud for ML, AI, and advanced analytics at scale.As referenced in this podcast, Amazon refers to Amazon Web Services and Google refers to Google Cloud Platform.

Business value is what we are going for at the end of the day, it's not the technology.Sudi Bhattacharya is a managing director at Deloitte Consulting LLP and a trusted C-Suite advisor who provides companies with a strategic roadmap to deliver big data-driven actionable insight at critical decision points. Sudi leads enterprise-level data and analytics transformation as a seasoned pragmatic thinker who builds programs to harvest data as a core asset and nurtures a next-generation culture that applies data insights to help grow Fortune 500 businesses in Finance, Retail, CPG, and Foodservice.

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