A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?
In a streaming deployment, the system is continuously running, which necessitates handling various data types without causing errors, as the flow of data is constant and real-time. It’s important to have rigorous data tests to ensure that any changes in data formats can be managed properly. Additionally, since the system is always on, there might not be immediate availability of practitioners to debug poor model performance on the fly, making preemptive data checks crucial. Lastly, the need for the system to autoscale to handle varying data volumes further underscores the importance of these checks. Therefore, all the listed statements contribute to the importance of data type tests and checks in streaming deployments.
The correct answer is B. All of these statements. In a streaming deployment, the system is always on and continuously processing data. Therefore, it’s crucial to handle all types of data without producing an error. Rigorous data tests and checks can help ensure that the system can handle changes in data formats and continue to operate smoothly. Additionally, because the system is always on, there may not be a practitioner available to debug poor model performance in real-time. Data tests and checks can help catch potential issues before they impact model performance. Finally, because the system is always on, it’s important to confirm that the deployment can autoscale to handle varying data volumes. Data tests and checks can help validate the system’s ability to scale.
makes more sense to me
Agree with Bokninja
agree with BokNinja
A is correct