Professional Cloud ArchitectDemo

Question

Your machine learning (ML) engineers rely on self-hosted Jupyter notebooks for work such as data preparation, model training, and fine-tuning. The operations team subsequently deploys these models across various environments. You want to give ML engineers maximum flexibility, encourage collaboration through a common toolset, and use Google Cloud’s scalability while adhering to Google-recommended practices. What should you do?

A Use AutoML for machine learning and Cloud Deploy for model deployment.
B Use Colab Enterprise for machine learning and DevOps for model deployment.
C Use Vertex AI for machine learning and machine learning operations (MLOps) for model deployment.
D Use TensorFlow for machine learning and Cloud Deploy for model deployment.

Explanation

Vertex AI provides a unified platform for data engineering, data science, and ML engineering workflows, enabling teams to collaborate with a common toolset and use Google Cloud’s scalable infrastructure. Its end-to-end MLOps capabilities support automating and scaling the ML lifecycle, including model deployment and management.

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