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
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