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Many ML systems benefit from having the feature store as their data platform, including: Interactive ML systems receive a user request and respond with a prediction. An interactive ML system either downloads a model and calls it directly or calls a model hosted in a model-serving infrastructure.
Image Source — Pixel Production Inc In the previous article, you were introduced to the intricacies of data pipelines, including the two major types of existing data pipelines. You also learned how to build an Extract Transform Load (ETL) pipeline and discovered the automation capabilities of Apache Airflow for ETL pipelines.
A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. DataIngestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.
It enables accessing, transforming, analyzing, and visualizing data on a single workstation. Databricks offers a cloud-based platform optimized for data engineering and collaborative analytics at scale. It brings together dataingestion, transformation, model training, and deployment in one integrated workflow.
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