A tool that can automate the entire process of creating and deploying an analytical model. It’s designed to help non-data scientists build analytical models, but data scientists may use it to accelerate their work.
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Data scientists should lead the selection of ML techniques because they understand the logical problem to solve. Business owners provide the guiding business objectives, and data engineers help ensure model fit with datasets. Data teams also should consider using an autoML tool, which recommends a technique based on their features and labels. When in doubt, choose the simpler technique to reduce risk.
- Kevin Petrie in The Machine Learning Lifecycle and MLOps: Building and Operationalizing ML Models - Part II
May 12, 2021 (Blog)
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