Machine Learning Engineering in Finance - Nemanja Radojkovic

We talked about:

• Nemanja’s background

• When Nemanja first work as a data person
• Typical problems that ML Ops folks solve in the financial sector
• What Nemanja currently does as an ML Engineer
• The obstacle of implementing new things in financial sector companies
• Going through the hurdles of DevOps
• Working with an on-premises cluster
• “ML Ops on a Shoestring” (You don’t need fancy stuff to start w/ ML Ops)
• Tactical solutions
• Platform work and code work
• Programming and soft skills needed to be an ML Engineer
• The challenges of transitioning from and electrical engineering and sales to ML Ops
• The ML Ops tech stack for beginners
• Working on projects to determine which skills you need

Links:

• LinkedIn: https://www.linkedin.com/in/radojkovic/

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