Exam Professional Machine Learning Engineer topic 1 question 129 discussion

Went with B

I believe it B. Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. Use standard TFX components for data validation and model analysis, and use Vertex AI Pipelines for model retraining. Because : - Solution A is not scalable and will be expensive to run. It also does not take advantage of managed services. Solution C is more scalable than option A, but it is still not as scalable as using TFX and Vertex AI Pipelines. It also does not take advantage of managed services. - Solution D is the most flexible, but it is also the most complex. It requires more knowledge of Apache Airflow and is more difficult to manage. Overall, the best solution to productionize the proof-of-concept ML model is to use TFX and Vertex AI Pipelines. This solution is scalable, reliable, and easy to manage. It also takes advantage of managed services, which can help to reduce costs.

B. Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. Use standard TFX components for data validation and model analysis, and use Vertex AI Pipelines for model retraining. The reason for this choice is that TFX and Vertex AI Pipelines provide a scalable and cost-effective solution for productionizing machine learning models. TFX is an end-to-end ML platform for building scalable and repeatable ML workflows, while Vertex AI Pipelines provides a fully managed service for orchestrating ML workflows at scale. By using TFX and Vertex AI Pipelines, you can automate the execution of the steps contained in the Jupyter notebook, and schedule the pipeline for weekly retraining. This approach also takes advantage of managed services, which helps to minimize cost.

All the others look really wrong, so B.

B (not sure)