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Machine Learning Engineering in Finance - Nemanja Radojkovic

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Manage episode 398149982 series 2831626
Content provided by DataTalks.Club. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by DataTalks.Club or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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:

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

  continue reading

198 episodes

Artwork
iconShare
 
Manage episode 398149982 series 2831626
Content provided by DataTalks.Club. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by DataTalks.Club or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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:

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

  continue reading

198 episodes

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