Building a machine learning model is a process that requires a lot of iteration and trial and error. For certain classes of problem a large portion of the searching and tuning can be automated. This allows data scientists to focus their time on more complex or valuable projects, as well as opening the door for non-specialists to experiment with machine learning. Frustrated with some of the awkward or difficult to use tools for AutoML, Angela Lin and Jeremy Shih helped to create the EvalML framework. In this episode they share the use cases for automated machine learning, how they have designed the EvalML project to be approachable, and how you can use it for building and training your own models.
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- Your host as usual is Tobias Macey and today I’m interviewing Angela Lin, Jeremy Shih about EvalML, an AutoML library which builds, optimizes, and evaluates machine learning pipelines
- How did you get introduced to Python?
- Can you describe what EvalML is and the story behind it?
- What do we mean by the term AutoML?
- What are the kinds of problems that are best suited to applications of automated ML?
- What does the landscape for AutoML tools look like?
- What was missing in the available offerings that motivated you and your team to create EvalML?
- Who is the target audience for EvalML?
- How is the EvalML project implemented?
- How has the project changed or evolved since you first began working on it?
- What is the workflow for building a model with EvalML?
- Can you describe the preprocessing steps that are necessary and the input formats that it is expecting?
- What are the supported algorithms/model architectures?
- How does EvalML explore the search space for an optimal model?
- What decision functions does it employ to determine an appropriate stopping point?
- What is involved in operationalizing an AutoML pipeline?
- What are some challenges or edge cases that you see users of EvalML run into?
- What are the most interesting, innovative, or unexpected ways that you have seen EvalML used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on EvalML?
- When is EvalML the wrong choice?
- When is auto ML the wrong approach?
- What do you have planned for the future of EvalML?
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