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Answer: Adopt Kubeflow on Google Kubernetes Engine (GKE) to leverage its open-source platform for creating and deploying ML workflows using Docker containers in a Kubernetes environment., Leverage AI Platform (currently Vertex AI), a suite of managed ML products on Google Cloud, designed for model training, evaluation, deployment, and version management.
Kubeflow Pipelines on Google Kubernetes Engine (GKE) offers a robust solution for managing ML workflows in a Kubernetes environment, facilitating experiment management and component reuse. AI Platform (Vertex AI) provides a comprehensive suite of managed ML services on Google Cloud, ideal for the entire ML lifecycle. While Scikit-Learn is powerful for standard ML algorithms, it lacks the infrastructure for managing ML pipelines and lifecycle. SageMaker, although capable, is an AWS service and not aligned with the Google Cloud preference. Therefore, Kubeflow on GKE and AI Platform (Vertex AI) are the most suitable solutions for the given requirements.
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Your company has been training and deploying several ML models using TensorFlow on on-prem servers. The increasing complexity and costs associated with managing the training, updating, and deployment of these models have become a significant challenge. You are now tasked with identifying a cloud-based solution that not only reduces operational costs but also enhances efficiency and scalability. The solution should seamlessly integrate with TensorFlow and support the entire ML lifecycle, including training, evaluation, deployment, and version management. Considering these requirements, which two solutions would best address your company's needs? (Choose two.)
A
Utilize Scikit-Learn for its simplicity and power, despite its lack of comprehensive ML lifecycle management capabilities.
B
Adopt Kubeflow on Google Kubernetes Engine (GKE) to leverage its open-source platform for creating and deploying ML workflows using Docker containers in a Kubernetes environment.
C
Implement SageMaker managed services, an AWS product, for end-to-end ML lifecycle management, despite the preference for Google Cloud services.
D
Leverage AI Platform (currently Vertex AI), a suite of managed ML products on Google Cloud, designed for model training, evaluation, deployment, and version management.