Scale or Fail: Getting Experiment Tracking Right with MLflow

Originally Aired October 6, 2022

Scalable and reliable experiment tracking with MLflow

Experiment tracking in machine learning is a challenging problem, as keeping track of datasets, algorithms, pre-processing steps, hyperparameters, tools and libraries is essential for maintaining a proper system of record. The open source code-first framework MLflow has been gaining popularity among data science practitioners as it enables them to easily compare thousands of experiments and brings a level of transparency and standardization to the way they run experiments. In this technical webinar we'll introduce MLflow, teach you about its core components and how they are used, and do a technical demo that shows experiments creation, execution, comparison, registration, and deployment. You will also learn about how MLflow is secured via Domino Integration, and what additional advantages the combination of an enterprise MLOps platform and MLflow brings to accelerate model velocity.

What's in store for you

Introduction of MLflow

Learn the core components and how they are used

MLflow Secured via Domino Integration

Additional advantages the combination of an enterprise MLOps platform and MLflow brings

30-Day Free Trial

Domino Enterprise MLOps platform access

Meet the speakers

Nikolay Manchev

Head of Data Science for EMEA at Domino Data Lab.

In this role, Nikolay helps clients from a wide range of industries tackle challenging machine learning use-cases and successfully integrate predictive analytics in their domain specific workflows. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King's College London. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks and computational neurobiology.

Sameer Wadkar

Principal Field Engineer at Domino Data Lab

Sameer Wadkar is a Principal Field Engineer at Domino Data Lab helping where he helps customers through all phases of ML Development and Deployment on the Domino DataLab Platform. He is also responsible for implementing frameworks to enable integration with third party systems, and frameworks which enable extending the functionality of the Domino Data Lab platform. He is lead architect for implementing the field solution for integrating MLflow with the Domino Data Lab platform.

Get in the MLflow with scalable experiment tracking