A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:
The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
A Data Scientist is training a model to predict whether a customer will purchase a new product.
They are experimenting with many algorithm types (e.g., Linear Regression, Logistic Regression, Decision Trees) and hundreds of hyperparameter combinations. They are using MLflow to track their runs, but managing and distinguishing between runs has become difficult. They need an MLFIow technique that will organize and trace these experiments and that will follow commonly- accepted design patterns. Which technique will fulfill these requirements?
A machine learning engineer is working on a fraud detection machine learning application. When a transaction is made with a credit card, the machine learning application will immediately process the data and make a prediction to determine whether or not to approve the transaction based on the probability that the transaction is fraudulent. Which deployment strategy can be used to meet these requirements?
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They notice that not all details and objects are automatically logged, and they will need to manually log some things. Which of the following will need to be manually logged when performing nested runs with Hyperopt and MLflow Autologging?
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