Building and Maintaining an ML Production Pipeline at SEB – Ala Tarighati, SEB AB

Premium content

Login or register to unlock the content

In this session, we will go through the steps required to build and maintain an ML Production pipeline at a financial organization, SEB AB. We will describe our approach for build and deployment of ML models into a Production environment. We will further share our experiences and challenges for ML life-cycle management of ML models, from a Data scientist’s perspective.

Key Takeaways

  • Machine Learning model into Production 
  • MLOPS, ML model life-cycle management 
  • Continuous Delivery, Continuous Integration, Continuous Training

Add comment

Highlight option

Turn on the "highlight" option for any widget, to get an alternative styling like this. You can change the colors for highlighted widgets in the theme options. See more examples below.


Instagram has returned empty data. Please authorize your Instagram account in the plugin settings .

Ivana Kotorchevikj

Categories count color


Small ads


  • Afro-deko-mono
  • Maria d'Odessa, touching
  • Maria d'Odessa au bâton de rouge-baiser
  • Maria d'Odessa & the red lipstick
  • Maria d'Odessa, soulful.
  • Peanuts
  • Celebrating the hundredth anniversary of Charles M. Schulz
  • À propos serendipity ...
  • Les libraires

Social Widget

Collaboratively harness market-driven processes whereas resource-leveling internal or "organic" sources.