Generative AI programs often fail at prioritization before they fail at technology. A company may collect dozens of ideas—customer chatbots, proposal generation, coding assistants, document […]
Google Cloud Certifications
Google Cloud GenAI Leader: Responsible Generative AI Governance
Generative AI governance is the operating system around AI adoption: who may use which models, what data may be supplied, which actions require approval, how […]
Google Cloud GenAI Leader: When RAG Makes Sense for Business
Retrieval-augmented generation is valuable when a generative AI application needs facts that live outside the model and those facts are too numerous, private, or changeable […]
Google Cloud GenAI Leader: Gemini Enterprise and Vertex AI
Google Cloud’s enterprise AI portfolio changed materially in 2026, so older diagrams that place Gemini Enterprise and Vertex AI in separate boxes can be misleading. […]
Google Cloud GenAI Leader: Improving Gemini Applications on Google Cloud
Improving a Gemini application is rarely a matter of jumping straight to fine-tuning. Most quality problems can be solved more cheaply and with less operational […]
Google Cloud PMLE: Building ML Pipelines with Vertex AI
A machine-learning notebook can train a useful model, but it is not yet an operational ML system. Production teams need the same sequence of preprocessing, […]
Google Cloud PMLE: Vertex AI Model Garden vs Custom Training
Choosing how to build an AI solution on Google Cloud starts before choosing a training machine or a model family. The first question is whether […]
Google Cloud PMLE: Model Monitoring on Vertex AI
A model can pass validation at release time and still become less useful later. Customer behavior changes, sensors drift, product catalogs evolve, fraud tactics adapt, […]
Google Cloud PMLE: Grounding and RAG on Google Cloud
Large language models can generate fluent answers without having access to the organization’s current private information. Retrieval-augmented generation, or RAG, addresses that gap by retrieving […]
Google Cloud PMLE: Responsible AI Evaluation
Model evaluation is often reduced to a single quality metric, but production AI can fail even when that metric improves. A classifier can be accurate […]
Google Cloud PDE: Choosing Dataflow, Dataproc, or BigQuery
Dataflow, Dataproc, and BigQuery all process large datasets on Google Cloud, but they solve different processing problems. Dataflow is a managed execution service for Apache […]
Google Cloud PDE: BigQuery Partitioning vs Clustering
BigQuery table design has a direct effect on how much data a query scans and how predictable that scan will be. Partitioning and clustering are […]
Google Cloud PDE: Pub/Sub Delivery Semantics
Reliable event ingestion depends on understanding what a messaging service guarantees and what the application must still guarantee for itself. Google Cloud Pub/Sub can provide […]
Google Cloud PDE: Dataplex Universal Catalog
Google Cloud’s data-catalog story has changed names. Dataplex Universal Catalog has transitioned to Knowledge Catalog, an AI-oriented catalog and context layer within Google’s broader data […]
Google Cloud PDE: Choosing Bigtable, Spanner, or BigQuery
Bigtable, Spanner, and BigQuery can all store very large amounts of data on Google Cloud, but that similarity is superficial. Bigtable is a wide-column, key-oriented […]