Databricks Updated Databricks-Machine-Learning-Professional Exam Questions and Answers by giovanni

 In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, the arrival of a new model version in the MLflow Model Registry is a common event that triggers the execution of automated testing. This is because the MLflow Model Registry is a centralized repository for managing the full lifecycle of MLflow models. It provides model lineage, model versioning, stage transitions, and annotations. When a new model version is registered, it can be automatically tested for quality, performance, and compliance using various tools and frameworks, such as MLflow Projects, MLflow Models, MLflow Tracking, and MLflow Model Serving. The automated testing can help ensure that the new model version meets the desired criteria and standards before it is deployed to production or other stages. The automated testing can also help detect and prevent any errors, bugs, or drifts that may affect the model functionality or accuracy. References:

• MLflow Model Registry Documentation, p. 1-2
• MLOps: Continuous delivery and automation pipelines in machine learning, Continuous integration and delivery (CI/CD) for ML, p. 5-6
• Machine Learning Engineering with MLflow, Testing and Validating ML Models, p. 1-2
• CI/CD for Machine Learning: What it is & Benefits in 2023 - AIMultiple, CI/CD for Machine Learning, p. 1-2

The correct code block is the one that matches the event type and the model name specified in the webhook. The event type is MODEL_VERSION_TRANSITIONED_TO_STAGING, which means the webhook will be triggered when a model version is transitioned to the staging stage. The model name is model, which means the webhook will only apply to the registered model with that name. Therefore, the code block that satisfies both conditions is:

client.transition_model_version_stage (name=model, version=model_version, stage=“Staging”)

This code block will transition the model version to the staging stage and invoke the webhook to run the associated job.

References: MLflow Model Registry Webhooks on Databricks, MLflow Python API

A model signature is a specification of the input and output schema of a model, which defines the names, data types, and shapes of the model inputs and outputs. Logging a model signature with an MLflow model has several benefits, such as:

• The signature can be used to document what type of data is expected as input and output by the model, which can help users and developers understand how to use the model correctly.
• The signature can be used to enforce schema validation when serving models via REST API, which can help prevent errors and ensure data quality. For example, Azure Machine Learning enforces compliance with the model signature for MLflow models that contain a signature1.
• The signature can be used to enable automatic conversion of input data to the appropriate format for the model, such as pandas DataFrame, numpy array, or PyTorch tensor. This can simplify the data preprocessing and inference steps and reduce the code complexity.

References:

• MLflow Models Documentation, Model Signature and Input Example, p. 6-7
• mlflow.models.signature Documentation, p. 1-2
• Guidelines for deploying MLflow models - Azure Machine Learning, p. 1