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

The MLflow Model Registry provides a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage, model versioning, model aliasing, model tagging, and annotations. The Model Registry defines four model stages that represent the lifecycle of a model: None, Staging, Production, and Archived. Each model stage has a different meaning and purpose:

• None: This is the default stage when a new model version is registered. It indicates that the model version is not ready for any downstream use cases and needs further development or testing.
• Staging: This stage indicates that the model version is ready for testing or validation in a pre-production environment. It can be used for quality assurance, user acceptance testing, or performance evaluation before moving to production.
• Production: This stage indicates that the model version is ready for deployment in a production environment. It can be used for serving predictions, scoring data, or supporting business decisions. Only one model version per registered model can be in the production stage at a time.
• Archived: This stage indicates that the model version is no longer in active use and can be safely deleted or moved to a long-term storage. It can be used for historical or audit purposes, or for restoring a previous model version if needed.

References:

• MLflow Model Registry Documentation, p. 1-2
• Introducing the MLflow Model Registry - Databricks, p. 1-2
• Manage MLflow Models — Databricks Documentation, p. 1-2

Batch deployment is the most common paradigm for machine learning projects, as it accounts for 80-90% of deployments1. Batch deployment leverages databases and object storage to process a large batch of input data at once, rather than processing each input data point individually in real time. Batch deployment is well suited for large data volume scenarios that need efficient processing but response time isn’t critical. For example, you might use batch deployment to generate recommendations for customers based on their purchase history, or to detect anomalies in sensor data collected over a period of time. Batch deployment is less expensive and more efficient than real-time deployment, as the underlying compute runs only during the batch job and can be scaled up or down as needed. Batch deployment can be implemented using Databricks Machine Learning endpoints, which provide a unified interface to invoke and manage model deployments across compute types2. References:

• Databricks Machine Learning Professional Exam Guide, Section 3: Model Deployment, p. 4
• Databricks Machine Learning Documentation, Deploying ML Models, Batch Inference, p. 1
• Machine Learning in Production - Databricks, The Four Deployment Paradigms, p. 9
• Deploy machine learning models in production environments, Deployment methods, p. 1

Concept drift in machine learning and data mining refers to the change in the relationships between input and output data in the underlying problem over time. In other domains, this change maybe called “covariate shift,” “dataset shift,” or “nonstationarity.” Concept drift can affect the performance and accuracy of predictive models that assume a static relationship between input and output variables. Concept drift can be caused by various factors, such as changes in user behavior, environmental conditions, market trends, etc. Concept drift can be detected and handled by various methods, such as periodic retraining, online learning, ensemble methods, etc References:

• Concept drift - Wikipedia
• A Gentle Introduction to Concept Drift in Machine Learning
• Model Drift & Machine Learning: Concept Drift, Feature Drift, Etc.
• Data Drift vs. Concept Drift: What Is the Difference?