Databricks Certified Machine Learning - Associate
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In a machine learning project, you have a trained model and a new batch of data to make predictions. Explain the concept of feature caching in a feature store and provide a step-by-step process to cache features for faster scoring in Databricks, including the necessary code snippets.
In a machine learning project, you have a trained model and a new batch of data to make predictions. Explain the concept of feature caching in a feature store and provide a step-by-step process to cache features for faster scoring in Databricks, including the necessary code snippets.
Simulated
A
Feature caching in a feature store is the process of storing pre-computed features in a cache to improve the performance and speed of the scoring process.
11.1%
B
To cache features for faster scoring in Databricks, first, retrieve the required features from the feature store table using the feature store API. Then, use a caching mechanism such as in-memory caching or distributed caching to store the features. Finally, use the cached features for scoring the model, which reduces the time taken to fetch the features from the feature store table.
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