Databricks Machine Learning Associate Question 264
Select 3You are tasked with deploying a machine learning model trained on Databricks to a production environment. Which of the following challenges are commonly associated with distributing machine learning models?
- A
Ensuring model reproducibility across different environments
- B
Managing dependencies and software versions required for the model
- C
Scaling the model inference to handle varying workloads
- D
Training the model with a large dataset on a single machine
- E
Ensuring that the deployed model remains unbiased over time
Show answer and explanation
Correct answers: A, B, C
Explanation
Distributing machine learning models involves multiple challenges, including ensuring reproducibility, managing dependencies, and scaling inference workloads. These factors are crucial to deploying models effectively in production environments and ensuring they perform as expected under real-world conditions.
- A. Correct.
Ensuring model reproducibility across different environments can be challenging due to differences in hardware, operating systems, and library versions.
- B. Correct.
Managing dependencies and software versions is often complex, as models may require specific libraries, versions, or configurations that must be replicated in production.
- C. Correct.
Scaling model inference is a common challenge, as production systems need to handle varying traffic loads efficiently while maintaining low latency.
- D. Incorrect.
Training the model with a large dataset on a single machine is not typically related to distributing models; it is a training phase concern rather than deployment-specific.
- E. Incorrect.
Ensuring that the deployed model remains unbiased over time is related to monitoring and retraining but is not directly associated with initial model distribution.