Super Data Science: ML & AI Podcast with Jon Krohn 917: 8 Steps to Becoming an AI Engineer, with Kirill Eremenko
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Aug 26, 2025 Kirill Eremenko, Founder of SuperDataScience, shares insights from his innovative AI engineering bootcamp. He outlines the curriculum's key topics, spanning from foundational Python skills to advanced AI system deployment. Kirill reveals how to utilize AI for business impact and explains the transformation of concepts into practical applications. He also discusses the importance of mindset shifts for AI engineers and highlights the role of employer sponsorship in education, providing a roadmap for aspiring tech educators.
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Measure Before You Build
- Define the business goal and success metric before building any AI solution.
- If you can't measure success, don't build the model yet.
Science Plus Deployment Equals Value
- Effective AI engineering blends AI science with production deployment skills.
- You must design for security, cost, reliability, and scalability, not just prototype accuracy.
Explore LLMs Early
- Compare many LLMs and test chat versus reasoning models in week one.
- Play with API calls and establish benchmarks specific to your business.
