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

Data type tests and checks are particularly important for streaming deployments because of the following reasons:

• Because the streaming deployment is always on, all types of data must be handled without producing an error. Streaming data may come from various sources and have different formats, schemas, or quality. If the streaming deployment cannot handle the data properly, it may result in errors, failures, or inaccurate outputs. Therefore, data type tests and checks can help validate the data and ensure the streaming deployment can process it correctly.
• Because the streaming deployment is always on, there is no practitioner to debug poor model performance. Streaming data may also change over time, affecting the model performance and accuracy. If the streaming deployment does not have a mechanism to monitor and evaluate the model performance, it may not be able to detect or correct any issues. Therefore, data type tests and checks can help identify any data drift or anomalies that may impact the model performance and trigger alerts or actions.
• Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale. Streaming data may have varying volumes and velocities, requiring the streaming deployment to scale up or down accordingly. If the streaming deployment does not have a way to adjust the resources and throughput based on the data demand, it may result in bottlenecks, delays, or inefficiencies. Therefore, data type tests and checks can help measure the data load and ensure the streaming deployment can autoscale effectively. References: Streaming Data, Data Validation, Model Monitoring, Autoscaling

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

A pyfunc model is a generic Python model that can be loaded and used to make predictions using any Python ML library. By logging a custom model class that implements the preprocessing logic as a pyfunc model, the same logic will be applied when loading the model and calling the predict method on new data. This ensures consistency and reproducibility of the model predictions. The fit method is not relevant for loading a logged model, as it is only used for training the model. The pyfunc model does not affect the parallelizability or the need for pipeline-like objects of the model deployment. References: mlflow.pyfunc, Creating custom Pyfunc models