Bridging the gap between DevOps & MLOps

Data science teams live in their own specialized world, working with data, running experiments, and building great models. The software deployment teams that take those models and use them to power production applications have a completely different focus and set of trusted tools.

How can we bring both the worlds together in order to effectively deploy and scale ML models?

In this talk, we will discuss ways to bring DevOps maturity and processes for AI/ML model management and operations through;

  • Model reproducibility & portability across local, dev, and prod environments
  • CI/CD and release management best practices that scientists can easily adopt
  • Real-time model monitoring & operational risk management



Meeta Dash


Meeta is a customer-centric product leader with a track record of launching innovative products that solve real business problems.

As VP Product at Verta, she is building a platform to help data