Exam Professional Machine Learning Engineer topic 1 question 59 discussion

Old answer is A. New answer (not available) would be Virtex AI experiments which comes with monitoring API inbuilt. https://cloud.google.com/blog/topics/developers-practitioners/track-compare-manage-experiments-vertex-ai-experiments

ANS: A https://codelabs.developers.google.com/codelabs/cloud-kubeflow-pipelines-gis Kubeflow Pipelines (KFP) helps solve these issues by providing a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. Cloud AI Pipelines makes it easy to set up a KFP installation.

A is an old answer. The platform is evolving. So B is the right answer.

A is not correct. Agreed that Kubeflow Pipelines is a powerful tool for running and managing ML workflows, but exporting metrics to an external file (e.g., CSV or JSON) requires extra manual work for managing the data and querying it. Kubeflow Pipelines do not have the same native integration with BigQuery for storing metrics, and querying via the Kubeflow API can be more complex than using BigQuery, especially when managing large-scale experiments. So I will go with B.

This is an old question, when Vertex AI didn't have Vertex AI Experiments. The old answer is A

Shoudlnt it be B? VAI has inbuilt VAI experiments and metadata to track metrics..

Should agree with A

A) Kubeflow pipelines

either A or C but going with C due to minimal effort

I agree with tavva_prudhvi that cloud monitoring is not the best option to do machine learning tracking, Metadata is a better option for that purpose

Option C suggests using AI Platform Training to execute the experiments and write the accuracy metrics to Cloud Monitoring. While Cloud Monitoring can be used to monitor and collect metrics from various services in Google Cloud, it is not specifically designed for machine learning experiments tracking. Using Cloud Monitoring for tracking machine learning experiments may not provide the same level of functionality and flexibility as Kubeflow Pipelines or AI Platform Training. Additionally, querying the results from Cloud Monitoring may not be as straightforward as using the APIs provided by Kubeflow Pipelines or AI Platform Training. Therefore, while Cloud Monitoring can be used as a general-purpose monitoring solution, it may not be the best option for tracking and reporting machine learning experiments.

Cloud monitoring may not be the most suitable option for tracking and reporting experiments, only because of this option C is out & I stick to A

Went with A

It is B

This is the question, Try out and choose what is the closet to this lab.Last updated Jan 21, 2023 https://codelabs.developers.google.com/vertex_experiments_pipelines_intro#0

Vertex AI Experiments + Cloud Monitoring for the metrics. It's C!

I like C https://cloud.google.com/monitoring/mql