Greg Michaelson AutoML as it should have always been | JupyterCon 2023

AutoML that truly automates the entire workflow from data exploration to model deployment, integrates seamlessly with other tools, and simplifies customization and pricing.

Key takeaways
  • AutoML should be developed to automate the entire machine learning workflow, from data exploration to model deployment.
  • Automated machine learning tools need to be flexible, allowing users to tweak the solution to fit their specific problem.
  • Most automated machine learning tools are limited in their ability to handle complex problems and require a lot of manual intervention.
  • Automated machine learning should be designed to make it easier for experts to build machine learning models, not just novices.
  • The biggest problem with automated machine learning is that it often requires a lot of manual effort and customization to achieve good results.
  • Automated machine learning tools should be able to handle complex problems and provide detailed explanations of the machine learning models.
  • The ability to parallelize machine learning models and to automate the process of building machine learning models is crucial.
  • Automated machine learning tools should be able to integrate with other tools and systems to provide a seamless workflow.
  • The pricing model for automated machine learning tools is a mess and needs to be simplified.
  • Automated machine learning tools should provide a way to generate code for model training and prediction.
  • Zerv is an automated machine learning platform that aims to provide a seamless workflow for building and deploying machine learning models.
  • Zerv’s Pipelines package is an open-source tool that allows users to automate the machine learning workflow and provides a way to generate code for model training and prediction.
  • Zerv’s Pipelines package is designed to make it easier for experts to build machine learning models and to provide a way to automate the machine learning workflow.