Orchestrating Reproducible AutoML Experiments using PyCaret, W&B, and Prefect


AutoML automates the most time-consuming tasks of machine learning model development, allowing data scientists to train effective models more quickly. Traditionally, building a model has required domain expertise and time consuming iteration. AutoML accelerates this process.

The advantages of AutoML can be summed up in three major points:
- Increases productivity by automating repetitive task. (Focus on the problem rather than the models)
- Prevents manual error creep
- Democratizes machine learning making ML accessible to everybody.

Learn how to pair traditional AutoML workflows with orchestration (the automated configuration, management, and coordination of data and models) and experiment tracking (management of a system of record for our data and models) to provide yourself with the tools to turn your machine learning models into an operational machine learning workflow which can plug into common DevOps platforms


Anish loves turning ML ideas into ML products. Anish started his career working with multiple Data Science teams within SAP, using traditional ML, deep learning, and recommendation systems before landing at Weights & Biases. With the art of programming and a little bit of magic, Anish crafts ML projects to help better serve our customers, turning “oh nos” to “a-ha”s!

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