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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.
- 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.