Databricks Machine Learning Associate Question 265
Select 3A data science team has trained a machine learning model in Databricks and now plans to deploy it across multiple regions to support a global user base. During the deployment process, they encounter challenges such as latency issues, managing model versions, and ensuring consistent predictions across regions. Which of the following are common difficulties associated with distributing machine learning models?
- A
Ensuring model versions are synchronized across all deployment locations
- B
Handling increased training time for the model on regional datasets
- C
Managing latency and ensuring fast inference for users in different regions
- D
Addressing potential discrepancies in data preprocessing pipelines across regions
- E
Guaranteeing that the model achieves 100% accuracy in all regions
Show answer and explanation
Correct answers: A, C, D
Explanation
Distributing machine learning models introduces challenges such as synchronizing model versions, ensuring low latency for global users, and maintaining consistent data preprocessing pipelines. Addressing these issues is critical to ensuring reliable and accurate model performance across all regions. However, challenges like increased training time or achieving perfect accuracy are either unrelated or unrealistic in this context.
- A. Correct.
Ensuring model versions are synchronized across all deployment locations is a common challenge, as different regions may inadvertently run different versions of the model, leading to inconsistent predictions.
- B. Incorrect.
Increased training time is generally not a direct issue during distribution, as the training process is typically completed before deployment.
- C. Correct.
Managing latency and ensuring fast inference is a key challenge for distributed models because users in different regions may experience varying network performance and response times.
- D. Correct.
Addressing discrepancies in data preprocessing pipelines is essential, as inconsistent preprocessing can result in different model outputs for the same input data.
- E. Incorrect.
Guaranteeing 100% accuracy is not realistic for any machine learning model and is not a challenge specific to distributed deployment.