Tag: Google Cloud Gen AI Series
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Google Cloud Generative AI vs the Field, the Verdict (Google Cloud Gen AI Series, Part 30)
The closing scorecard on Google Cloud generative AI. Where Vertex AI and Gemini win, where they fall short, and how I would choose against AWS Bedrock and Azure AI Foundry in 2026.
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Reference Architectures on Google Cloud, from Chatbot to Batch (Google Cloud Gen AI Series, Part 29)
Most generative AI on Google Cloud reduces to four shapes: a direct call, RAG, an agent, and batch. Here is how each maps to managed services, what it costs, and which one to reach for first.
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Vertex AI Pipelines for LLMOps, from Notebook to Nightly Retrain (Google Cloud Gen AI Series, Part 28)
How I turn a Gemini tuning notebook into a Vertex AI pipeline that reruns nightly, gates on evals, registers versions, and does not quietly ship a worse model.
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Vertex AI Responsible AI, Governance, and Audit Logging (Google Cloud Gen AI Series, Part 27)
Data Access audit logs, SynthID provenance, Model Registry, and Access Transparency, wired into a governance baseline for a Gemini workload on Vertex AI. What is on by default, what you have to switch on, and what it costs.
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Vertex AI Cost Governance and FinOps (Google Cloud Gen AI Series, Part 26)
Read a Vertex AI bill line by line, then cut it with model tiering, context caching, Batch mode, and provisioned throughput judged against a real break-even. With budgets, billing export, and label-based attribution.
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Vertex AI Observability and Tracing, from Dashboard to Span (Google Cloud Gen AI Series, Part 25)
A green status code on an eight second request tells you nothing. Here is how Cloud Monitoring, Cloud Trace, and Cloud Logging on Vertex AI tell you which call was slow, what it cost, and what to never log.
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Multimodal on Vertex AI, from Nano Banana to Veo and Lyria (Google Cloud Gen AI Series, Part 24)
A field guide to generative media on Vertex AI: when to use Gemini native image generation, Imagen, Veo, Lyria, and Chirp, with real model IDs and a per-image cost model.
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Vertex AI Gen AI Evaluation Service, Pointwise to Trajectory (Google Cloud Gen AI Series, Part 23)
The Vertex AI Gen AI evaluation service turns a two-example spot check into a number you can gate a release on. How pointwise, pairwise, rubric, and trajectory metrics work, and how to validate the autorater judge before you trust it.
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Gemini Enterprise and Agentspace, Enterprise Agents for Every Employee (Google Cloud Gen AI Series, Part 22)
Google Agentspace is now Gemini Enterprise, the per seat front door that puts search and a gallery of agents in front of every employee. What it includes, how a request flows, what a seat costs, and when to buy it instead of building your own.
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Multi-Agent Systems on Vertex AI with ADK and Agent Engine (Google Cloud Gen AI Series, Part 21)
One big agent with twenty tools rots fast. Here is how to split it into a coordinator and typed sub-agents with the ADK, choose deterministic versus LLM-driven flows, connect across boundaries with A2A, and deploy to Vertex AI Agent Engine.
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TPU Pods and Multislice Distributed Training on GKE (Google Cloud Gen AI Series, Part 19)
Where a single TPU slice stops fitting your model, Multislice takes over. How v6e Pods, ICI and the DCN, and GKE JobSets scale training from 16 chips to thousands.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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