Databricks Databricks-Machine-Learning-Professional Exam With Confidence Using Practice Dumps

MLflow provides a module called mlflow.shap that can be used to automatically calculate and log a Shapley feature importance plot using the SHAP library. The mlflow.shap.log_explanation function takes a predict function and a sample of input data, and computes the SHAP values and base values for each feature. It then logs the following artifacts to the current run:

• Base values
• SHAP values (computed using shap.KernelExplainer)
• Summary bar plot (shows the average impact of each feature on model output) The mlflow.shap.log_explanation function can be used with any model that has a predict function, such as scikit-learn, xgboost, etc1234 References:
• mlflow.shap — MLflow 2.9.1 documentation
• Extract feature importance from a mlflow 1.9 PyFuncModel model
• 9.6 SHAP (SHapley Additive exPlanations) | Interpretable Machine Learning
• load MLFlow model and plot feature importance with feature names

To log feature importance data from a CSV file as an artifact of an MLflow run, the correct method is mlflow.log_artifact, which takes the local file path and the artifact path as arguments.The mlflow.log_model method is used to log a model with a specific flavor, such as pyfunc or spark. The mlflow.log_data method does not exist. The mlflow.log_model_and_data method also does not exist, and it is not necessary to log both the model and the data in this case. References:

• MLflow Tracking API
• MLflow Models

Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows users to express streaming computations the same way as batch computations on static data, using DataFrame and Dataset APIs. Structured Streaming can handle both unbounded and bounded data sources, and can process data in micro-batches or continuously. Structured Streaming can be used to provide continuous processing of data for machine learning applications, such as data ingestion, preprocessing, feature engineering, and model inference. StructuredStreaming can also integrate with MLflow and Delta Lake to enable end-to-end machine learning pipelines with tracking, reproducibility, and governance123 References:

• Structured Streaming Programming Guide - Spark 3.2.0 Documentation
• Structured Streaming + Kafka Integration Guide (Kafka broker version 0.10.0 or higher) - Spark 3.2.0 Documentation
• Machine Learning with Structured Streaming - Databricks
• [Continuous Machine Learning with Structured Streaming and MLflow - Databricks]