In this section, we provide a high-level overview of a typical workflow for machine learning-based software development. Generally, the goal of a machine learning project is to build a statistical model by using collected data and applying machine learning algorithms to them. Therefore, every ML-based software includes three main artifacts: Data, ML Model, and Code. Corresponding to these artifacts, the typical machine learning workflow consists of three main phases:
🔹Data Engineering: data acquisition & data preparation, 🔹ML Model Engineering: ML model training & serving, and 🔹Code Engineering: integrating ML model into the final product.
In this section, we provide a high-level overview of a typical workflow for machine learning-based software development. Generally, the goal of a machine learning project is to build a statistical model by using collected data and applying machine learning algorithms to them. Therefore, every ML-based software includes three main artifacts: Data, ML Model, and Code. Corresponding to these artifacts, the typical machine learning workflow consists of three main phases:
🔹Data Engineering: data acquisition & data preparation, 🔹ML Model Engineering: ML model training & serving, and 🔹Code Engineering: integrating ML model into the final product.
BY Data Engineering / Инженерия данных / Data Engineer / DWH
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