The author further discusses the benefits of separating the science from the AI pipelines through containerization for data scientists and researchers. Generative AI is likened to a flavor enhancer for work, improving the quality of outcomes. The author also suggests that generative AI can be used to great advantage in enterprise IT, particularly with proprietary data and specific use cases. The article concludes by emphasizing the need for infrastructure that provides the elasticity and agility needed to support AI evolution.
Key takeaways:
- Generative AI is part of the AI evolution and should be seen as a tool that complements human work rather than replacing it. It can aid in tasks such as customer behavior analytics and recommendation engines to predictive maintenance.
- Data science machine learning is evolving as machine learning grows across industries, with a shift from predictive models to a more dynamic, data-centered discipline. Containers and Kubernetes can play an important role in accelerating AI research.
- Containerization can be beneficial to data scientists and AI researchers by separating the science from the AI pipelines. This allows researchers to focus on their domain expertise and hand off the running of their code to others.
- Generative AI can be used to enhance work quality by providing additional research and insights. In enterprise IT, it can be used for internal and customer-facing use cases, and to create test cases for new capabilities based on accurate data.