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Question 102/112
You are building a MLOps platform to automate your company's ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines How should you store the pipelines' artifacts'?
Correct Answer: C
To organize the artifacts for dozens of pipelines, you should store the parameters in Vertex ML Metadata, store the models' source code in GitHub, and store the models' binaries in Cloud Storage. This option has the following advantages:
* Vertex ML Metadata is a service that helps you track and manage the metadata of your ML workflows, such as datasets, models, metrics, and parameters1. It can also help you with data lineage, model versioning, and model performance monitoring2.
* GitHub is a popular platform for hosting and collaborating on code repositories. It can help you manage the source code of your models, as well as the configuration files, scripts, and notebooks that are part of your ML pipelines3.
* Cloud Storage is a scalable and durable object storage service that can store any type of data, including model binaries4. It can also integrate with other services, such as Vertex AI, Cloud Functions, and Cloud Run, to enable easy deployment and serving of your models5.
References:
* 1: Introduction to Vertex ML Metadata | Vertex AI | Google Cloud
* 2: Manage metadata for ML workflows | Vertex AI | Google Cloud
* 3: GitHub - Where the world builds software
* 4: Cloud Storage | Google Cloud
* 5: Deploying models | Vertex AI | Google Cloud
* Vertex ML Metadata is a service that helps you track and manage the metadata of your ML workflows, such as datasets, models, metrics, and parameters1. It can also help you with data lineage, model versioning, and model performance monitoring2.
* GitHub is a popular platform for hosting and collaborating on code repositories. It can help you manage the source code of your models, as well as the configuration files, scripts, and notebooks that are part of your ML pipelines3.
* Cloud Storage is a scalable and durable object storage service that can store any type of data, including model binaries4. It can also integrate with other services, such as Vertex AI, Cloud Functions, and Cloud Run, to enable easy deployment and serving of your models5.
References:
* 1: Introduction to Vertex ML Metadata | Vertex AI | Google Cloud
* 2: Manage metadata for ML workflows | Vertex AI | Google Cloud
* 3: GitHub - Where the world builds software
* 4: Cloud Storage | Google Cloud
* 5: Deploying models | Vertex AI | Google Cloud
- Question List (112q)
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