Databricks Machine Learning Associate Question 266
Select 3A data science team has trained multiple machine learning models and wants to deploy them at scale using Databricks. However, they are encountering challenges such as dependency conflicts, model versioning issues, and performance inconsistencies across environments. Which of the following are valid difficulties associated with distributing machine learning models?
- A
Ensuring the deployed model runs consistently across different environments.
- B
Handling dependency conflicts between the training and deployment environments.
- C
Reducing the size of the training dataset for quicker experimentation.
- D
Managing and versioning models for updates and rollbacks.
- E
Improving the accuracy of the model after deployment.
Show answer and explanation
Correct answers: A, B, D
Explanation
Distributing machine learning models involves multiple challenges, including ensuring environmental consistency, resolving dependency conflicts, and implementing robust model versioning systems. These issues must be addressed to ensure smooth deployment and proper functionality of machine learning models at scale.
- A. Correct.
Ensuring consistency across environments is a common difficulty as compatibility issues can arise between local, staging, and production setups.
- B. Correct.
Dependency conflicts often occur if the versions of libraries or frameworks used during training differ from those in the deployment environment.
- C. Incorrect.
Reducing the size of the training dataset is unrelated to distributing machine learning models; it is more relevant to training optimization.
- D. Correct.
Managing and versioning models for updates, rollbacks, and reproducibility is a key challenge in distributed machine learning workflows.
- E. Incorrect.
Improving the accuracy of the model is part of the model development process, not a specific challenge associated with distributed deployment.