Skip to content

Call For Speakers

We are looking for data science leaders, practitioners, and other industry professionals who want to highlight the breakthroughs they’re making in data science and MLOps.

Apply Now


Learn more about the three different speaker sessions tracks and topic suggestions.

Data Science Leaders: Suggested Topics

Best practices and transformational strategies to make data science leaders and their teams more productive and satisfied, while increasing knowledge sharing and efficiency.

  • Understanding and removing the hidden barriers to enterprise data science
  • Navigating the journey to a model-driven business
  • Managing your team across the enterprise data science life cycle
  • Standardizing processes across data science teams & tools
  • Practical strategies to prioritize and apply AI projects
  • Increasing the capacity of your data science teams
  • Compounding data science value with user adoption & collaboration
  • IT & data science partnership strategies for success


Model Driven Businesses: Suggested topics

Applications of data science, machine learning and MLOps supporting organizational strategy, operational efficiency and significant business value.

  • Don’t fight fires, prevent fires: Using ML to change the paradigm
  • Leading from the top: The role of the CEO in driving data science
  • Where should you be exploring next with data science?
  • Model Velocity, what it means, and why you need it
  • Moving the needle on business adoption and allowing for failure
  • Data scientists as storytellers: Making the math exciting
  • When IT and data science work together at scale
  • Industry panel on ethics, transparency, AutoML, and tracking

MLOPs Tips, Tools and Techniques: Suggested Topics

Techniques, tools, and advice across the entire MLOps lifecycle from exploration and experimentation to deployment and monitoring.

  • Practical updates and advice on popular tools
  • New research on modeling approaches
  • AI ethics & explainable AI for deep nets?
  • Practical updates and advice on NLP and transformer-based models
  • Tips for making transfer learning work in the wild
  • Finding the right acceleration framework for the job (Spark, Ray, Dask, RAPIDS, etc.)
  • How to get the most out of GPU-based analytics
  • Best practices for working with data science IDEs
  • The latest applicable trends in model monitoring
  • Tips for establishing viable model validation flows
  • How to get the most out of source control repositories and CI/CD pipelines

Do you have a talk that you would like to give at Rev 3? Apply to share your insights and ideas as part of our speaker line-up!