After deploying a machine learning model into production within Azure Databricks, how can you ensure it scales effectively with increasing data volumes and user requests?

Real Exam

Implementing custom scalability testing scripts within Databricks notebooks that incrementally increase data load and request rates, monitoring model performance and resource utilization

9.0%

Leveraging Databricks‘ MLflow for model tracking, combined with Azure Kubernetes Service (AKS) to simulate scalable deployment scenarios

25.6%

Conducting A/B testing with varying sizes of data inputs and concurrent requests, using Azure Event Hubs to generate traffic and Azure Monitor for performance insights

19.2%

Utilizing Azure Machine Learning‘s model deployment and management capabilities to simulate load scenarios and gather performance metrics for analysis

46.2%

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After deploying a machine learning model into production within Azure Databricks, how can you ensure it scales effectively with increasing data volumes and user requests?