Kubeflow, MLFlow and beyond — augmenting ML delivery

Россия, Kubeflow, MLFlow and beyond — augmenting ML delivery

Welcome to a new free #ODSC webinar!

This time we’ll focus on #MLFlow. In this talk, we’ll:

  1. Design a reference machine learning workflow;
  2. Review open source tools that contribute to this workflow and are applicable to build reproducible automation of it.

Reproducible ML pipelines in research and production with monitoring insights from live inference clusters could enable and accelerate the delivery of AI solutions for enterprises. There is a growing ecosystem of tools that augment researchers and machine learning engineers in their day to day operations.

Still, there are big gaps in the machine learning workflow when it comes to training dataset versioning, training performance and metadata tracking, integration testing, inferencing quality monitoring, bias detection, concept drift detection and other aspects that prevent the adoption of AI in organizations of all sizes.


  • A deeper view on traps and pitfalls on each stage of ML lifecycle.
  • Reference implementation and automation of ML Workflow.

Prerequisite knowledge:

  • Understanding of core Data Science methods, frameworks and libraries.
  • An image of what Docker and Kubernetes are.

Speaker — Stepan Pushkarev, CTO of Provectus.
His background is in the engineering of data platforms.
Stepan spent the last couple of years building continuous delivery and monitoring tools for machine learning applications as well as designing streaming data platforms.
He works closely with data scientists to make them productive and successful in their daily operations.

Дата и время

July 16 - July 16




Kubeflow, MLFlow and beyond — augmenting ML delivery

Zoohackathon 2019

Kubeflow, MLFlow and beyond — augmenting ML delivery / Москва


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Zoohackathon 2019
JFuture Conference 2019

Горячие вакансии

Россия, Москва
Field инженер