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MLOps in Corporations and Startups - Nemanja Radojkovic
Mar 14, 2025
Nemanja Radojkovic, a Senior Machine Learning Engineer at Euroclear, shares his expertise in MLOps across both startups and corporations. He discusses the balance between flexibility and stability in different work environments, highlighting the unique challenges of implementing MLOps on tight budgets. The conversation covers navigating cloud complexities, the importance of selecting the right tools, and practical career advice for aspiring data scientists. Nemanja emphasizes mentorship and the evolving landscape of technology, making compelling points for both paths.
58:03
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Quick takeaways
- MLOps in startups fosters rapid experimentation and prototyping, contrasting with the bureaucratic constraints often found in large corporations.
- The speaker emphasizes the importance of managing technical debt in fast-paced startup environments to ensure long-term project maintainability.
Deep dives
Career Journey and Transition to MLOps
The speaker discusses their background in electrical engineering and subsequent shift to bioengineering for a PhD in Belgium, revealing that academic life was not a suitable fit. After transitioning into consulting with Deloitte, they began exploring machine learning engineering, which involved data wrangling and model prototyping. The speaker highlights the emergence of MLOps as a necessary practice even before it became a formal term in the industry, emphasizing their self-driven learning in Python and deployment practices. Their career saw significant roles in various financial institutions, demonstrating their extensive experience across sectors over the past decade.
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