DataTalks.Club

Machine Learning Engineering in Finance - Nemanja Radojkovic

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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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