Test Campaign

1 submitted in this role.

The Cloud Architect is highly confident in designing complex AI architectures for a multi-cloud (AWS/GCP) hybrid environment, with specific plans for GenAI expansion in the next 6-12 months. Training should focus on advanced architectural best practices, hybrid design patterns, and formalizing knowledge around Generative AI implementations to support the upcoming growth.

Priority	Type of course	Course title	Rationale
High % of this role	Google Cloud/GenAI	938: Architecting and Implementing Generative AI Solutions in the Cloud	This course directly supports the organization's planned GenAI expansion by focusing on architectural implementation in a cloud environment.
High % of this role	Google Cloud	Professional Cloud Architect Certification Workshop	Certifying architectural skills specifically on Google Cloud, the platform used for ML training, solidifies expertise for complex designs.
Medium % of this role	AWS	Advanced Architecting on AWS	Reinforces expertise on the secondary cloud platform (AWS) to ensure robust and consistent design across the multi-cloud hybrid environment.
Medium % of this role	AWS	AWS Well-Architected Best Practices	Provides formal governance and operational best practices for maintaining high standards in cloud deployments across the organization.

1 submitted in this role.

The ML Engineering team exhibits a significant MLOps maturity gap, specifically noting they only track production weights, indicating a lack of robust experiment tracking and governance. Models are primarily trained on Google Cloud, requiring immediate training focused on streamlining development lifecycles and modern ML operations (MLOps).

Priority	Type of course	Course title	Rationale
High % of this role	Google Cloud ML	Vertex AI for Machine Learning Practitioners	Addresses the urgent need for MLOps maturity, teaching how to leverage Google Cloud's managed platform for robust experiment tracking and model management.
High % of this role	Google Cloud ML	Professional Machine Learning Engineer Certification Workshop	Provides a comprehensive understanding of ML engineering principles, infrastructure, and governance required to fix maturity gaps.
Medium % of this role	Google Cloud/GenAI	Generative AI in Production	Prepares the team for robust production deployment of Generative AI models, aligning with the organization's planned expansion into GenAI technology.
Low % of this role	AWS ML	MLOps Engineering on AWS	Offers exposure to MLOps methodologies on the secondary cloud platform (AWS) to facilitate standardized pipelines across the multi-cloud environment.

1 submitted in this role.

The Security Engineer has low familiarity with AI-specific risks (prompt injection, model abuse) despite evaluating AI for security operations and having formal data policies. Given the imminent GenAI expansion, focused training on securing GenAI workloads and strengthening multi-cloud security posture is crucial.

Priority	Type of course	Course title	Rationale
High % of this role	Google Cloud/Security	Vertex AI and Generative AI Security	Directly targets the identified gap by teaching the Security Engineer how to address GenAI-specific risks within the primary AI cloud platform.
High % of this role	AWS Security	Security Engineering on AWS	Builds advanced security engineering skills on AWS, ensuring the security team can defend AI workloads on both major cloud platforms.
Medium % of this role	Google Cloud/Security	Professional Cloud Security Engineer Certification Workshop	Provides a comprehensive, certified understanding of security governance and infrastructure best practices on Google Cloud.
Low % of this role	Security	Web and Mobile Application Security	Strengthens general application security fundamentals relevant to protecting interfaces that interact with GenAI models (e.g., prompt injection defense).

Form URL

Responses

Analyze

Executive summary

Key findings

Strategic implications

Role course recommendations

Export Summary