Databricks Machine Learning Associate Question 267
Select 4A team is deploying a machine learning model using Databricks to a distributed system for real-time inference. Which of the following challenges might they face during this process?
- A
Ensuring consistent model performance across different compute environments
- B
Managing model versioning and rollback in case of deployment failures
- C
Scaling the model to handle high-throughput, low-latency requests effectively
- D
Guaranteeing that the model achieves 100% accuracy in predictions
- E
Securing sensitive data used during model inference in the distributed system
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Distributing machine learning models comes with challenges such as ensuring consistent performance in varying environments, managing model lifecycle (versioning and rollback), scaling for performance, and securing sensitive data. While achieving perfect accuracy is desirable, it is not a practical or realistic challenge tied to the distribution process.
- A. Correct.
Ensuring consistent model performance across different compute environments is a common challenge as variations in hardware, software, or libraries can lead to inconsistencies in predictions.
- B. Correct.
Managing model versioning and rollback is critical in distributed systems to ensure that if a new model deployment fails, the system can revert to a stable previous version.
- C. Correct.
Scaling the model effectively to handle high-throughput and low-latency requests is a significant challenge, especially in real-time distributed systems, as it requires careful resource management and optimization.
- D. Incorrect.
Guaranteeing 100% accuracy in predictions is unrealistic and not a practical challenge associated with deployment, as no model can achieve perfect accuracy in real-world scenarios.
- E. Correct.
Securing sensitive data during model inference in a distributed system is an important challenge to ensure compliance with data privacy and security standards.