Vertex AI and Generative AI Implementation on GCP
Strengths
Broad Generative AI Coverage
The curriculum includes specific modules for text generation with Gemini, prompt engineering, embeddings, and multimodal processing involving images and video.
End-to-End ML Workflows
Instruction covers the full lifecycle from dataset creation and AutoML to custom training with containers and Vertex AI Pipeline construction.
Limitations
Technical Debt and API Mismatches
Learners have reported encountering deprecated APIs and code that requires manual updates to function in current environments. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
Interface Discrepancies
The Vertex AI Studio user interface has changed significantly compared to the visual demonstrations provided in the lessons. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
Instructional Gaps
Some signals suggest a lack of deep conceptual context during code execution and difficulty following lessons due to missing centralized code resources. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
Best suited to
- Learners seeking exposure to Vertex AI pipelines
- Developers interested in Gemini and multimodal processing
- Individuals preparing for Google Cloud ML Engineer certification
Less suited to
- Those seeking a seamless, copy-paste coding experience
- Learners who prefer highly detailed conceptual explanations
- Users wanting to avoid troubleshooting outdated API versions