Exam Professional Machine Learning Engineer topic 1 question 37 discussion

A. AI platform provides lower infrastructure overhead and allows you to not have to refactor your code too much (no containerization and such, like in KubeFlow).

A) AI Platform

The most suitable option for minimizing code refactoring and infrastructure overhead while enabling large-scale training on Google Cloud is: A. Use AI Platform for distributed training. * **Simplified Workflow:** AI Platform offers a managed service for training machine learning models. You can train your existing TensorFlow Estimator code with minimal changes, reducing the need for extensive code refactoring. * **Distributed Training:** AI Platform automatically handles distributing your training job across multiple machines, allowing you to leverage the power of Google's cloud infrastructure to train on large datasets efficiently. * **Reduced Infrastructure Overhead:** You don't need to manage the underlying infrastructure (e.g., setting up and maintaining a cluster) yourself. AI Platform takes care of all the infrastructure provisioning and management, minimizing the workload on your team.

I chose A. Even though D is a working option, it requires us to create a GKE cluster, which requires more work.

A - because it has native support for TF

A. Use AI Platform for distributed training. : Managed , low infra change migration: yes , although need code refactoring to bigquery sql B. Create a cluster on Dataproc for training. : only cluster ? what about training? C. Create a Managed Instance Group with autoscaling. : Same Q? D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster : only training?

Went with A

Option A is the best choice as AI Platform provides a distributed training framework, enabling you to train large-scale models faster and with less effort

using options eliminations answer between A,D will vote for A as it is easier

- using options eliminations answer between A,D will vote for A as it is easier

The answer is A. AI platform also contains kubeflow pipelines. you don't need to set up infrastructure to use it. For D you need to set up a kubernetes cluster engine. The question asks us to minimize infrastructure overheard.

D- Kubeflow pipelines with Vertex ai provides you ability to reuse existing code using a TF conatiner in a pipeline. it helps automate the process. there is a qwiklab walking through this. A-incorrect, question is asking resuse existing code with minimum changes. distributed deployment does not address that.

A - better to go with managed service and distributed

I am 100% sure that the answer is D. Kubeflow pipelines were designed keeping: A) Portability. B) Composability. C) Flexibility in mind. This is the pain point that the kubeflow pipelines address

TensorFlow Estimators means require distributed and that is key feature for AI platform or later Vertex AI.

I think is A

I think the answer is either A or B, but personally think it is likely B because dataproc is a common tool box on GCP used for ML while AI platform might require refactoring. However, I dont really know A or B