Google Cloud Professional Machine Learning Engineer — knowledge map
The 152 core concepts of Google Cloud Professional Machine Learning Engineer and how they connect. Click a node in the map above to explore related terms and prerequisites; the list below indexes every concept with its definition and links to its prerequisites and related concepts.
Concepts (152)
Vertex AI
A unified AI platform to build, train, deploy, and operate ML models end to end, including using foundation models.
Prerequisites: Foundation model
BigQuery
Google Cloud serverless data warehouse for fast SQL analytics on petabyte-scale data.
Cloud Storage
Object storage for unstructured data such as images, video, and backups.
Managed services (management boundary)
A categorization by how much AWS operates for you. The more fully managed (AWS handles patching/scaling/availability), the lower your operational burden but less control; unmanaged (e.g., EC2) is flexible but self-operated. It frames “where your responsibility ends” in the shared responsibility model.
Cloud Run
A service that runs containers serverless, scaling to zero when idle and billing only for usage.
Prerequisites: ML data preparation (encoding/scaling/imbalance)
Vertex AI Workbench
Google Cloud's managed JupyterLab-based ML development environment. It supports enterprise-grade security controls—VPC Service Controls, private Google access, least-privilege service accounts—for serious ML development, with native integration to BigQuery and Cloud Storage built in.
Prerequisites: BigQuery、Cloud Storage、Vertex AI、Service account
Gemini
Google Cloud core generative AI model for text generation, summarization, code assistance, and image understanding, embedded across services and Workspace.
Tabular Workflows
A feature that decomposes the tabular AutoML pipeline into components on Vertex AI Pipelines, letting you customize and reuse individual stages such as feature engineering or architecture search. Where plain AutoML is more black-box, Tabular Workflows targets advanced use cases needing fine control or scale over specific stages.
Prerequisites: Workflows、Vertex AI、Feature engineering and embeddings/RAG、Vertex AI Pipelines (Kubeflow/TFX)
Related: Vertex AI AutoML
Foundation model
A large pretrained model usable across many tasks (e.g., GPT); multimodal handles multiple data types.
ML data preparation (encoding/scaling/imbalance)
Shaping raw data for learning. Encoding turns categories numeric (one-hot/ordinal/target); scaling = standardize (z-score)/normalize (Min-Max); imbalance = SMOTE oversampling/class weights (evaluate with F1/PR-AUC). Fit transforms only on post-split training data (prevent leakage).
VPC Service Controls
A mechanism that prevents data exfiltration to managed services (e.g., BigQuery, Cloud Storage) via a service perimeter; a layer of control separate from IAM grants.
Prerequisites: BigQuery、Cloud Storage、Managed services (management boundary)
Cloud Composer
Managed orchestration based on Apache Airflow; schedules and runs multi-step pipelines with dependencies.
Workflows
Serverless orchestration for lightweight cross-service step chaining (simple workflows); heavy dependencies use Cloud Composer.
Prerequisites: Cloud Composer
BigQuery ML
A capability to create ML models and run predictions with only SQL on data in BigQuery, without moving the data.
Prerequisites: BigQuery
Compute Engine
Google Cloud IaaS providing virtual machines (VMs) with selectable CPU/memory and OS-level control; ideal as a lift-and-shift target.
Prerequisites: Migration strategy (lift-and-shift/improve-and-move/refactor)
Cloud Build
A serverless CI service that automates fetching source, building, testing, and containerizing images. Build steps are defined in cloudbuild.yaml and can be triggered by pushes to GitHub/Cloud Source Repositories; artifacts are typically stored in Artifact Registry.
Prerequisites: YAML
Related: Cloud Deploy、Artifact Registry
Service account
A special identity for workloads (VMs/apps) rather than humans; prefer short-lived tokens via metadata or Workload Identity Federation over downloaded keys.
Deployment strategies (canary/blue-green/rollback)
Canary = release to a subset first; blue/green = instant switch between old/new environments; rollback = revert quickly on issues. Risk-limiting release techniques.
Vertex AI AutoML
A Vertex AI capability that auto-builds high-quality custom ML models from tabular, text, image, or video data with minimal code. It runs neural architecture search and hyperparameter tuning under the hood to produce a model balancing accuracy against training cost. For quick SQL-only analysis, BigQuery ML can be the simpler choice.
Prerequisites: BigQuery、BigQuery ML、Vertex AI、Distributed training (Reduction Server/Horovod) and hyperparameter tuning
Related: Tabular Workflows
Eventarc
A service that routes events from diverse sources (Pub/Sub, Cloud Storage changes) uniformly to Cloud Run/Cloud Functions.
Prerequisites: Cloud Functions、Cloud Run、Cloud Storage、Pub/Sub
Ops Agent
An agent installed on Compute Engine or on-prem VMs that collects detailed system metrics (CPU/memory/disk, etc.) and logs into Cloud Monitoring / Cloud Logging. It unifies the legacy separate Monitoring agent and Logging agent—use this for new deployments.
Prerequisites: Cloud Logging、Cloud Monitoring、Compute Engine、Agent (agentic AI)
Dataproc
A managed service to run existing Spark/Hadoop workloads; differs in purpose from Dataflow (Beam).
Prerequisites: Dataflow、Managed services (management boundary)
Encryption keys (GMEK / CMEK / CSEK)
GMEK = Google-managed default keys; CMEK = customer-managed keys via Cloud KMS (control rotation/disabling); CSEK = customer-supplied keys. Data is encrypted at rest and in transit by default.
Related: Cloud KMS
Cloud SQL
A managed relational database compatible with MySQL/PostgreSQL/SQL Server, for typical business apps.
Fine-tuning
Further-training a foundation model on your labeled data to bake behavior/style/domain knowledge into its weights; heavier than RAG.
Prerequisites: Foundation model
Agent (agentic AI)
AI that, beyond returning text, autonomously plans, uses tools, and acts over multiple steps toward a goal.
Model Garden
A Vertex AI catalog to browse and choose among diverse foundation models (Google, open, third-party).
Prerequisites: Vertex AI、Foundation model
Cloud Deploy
A managed continuous-delivery (CD) service for GKE, Cloud Run, and Anthos. It templates a delivery pipeline (e.g., dev → staging → prod) and provides deployment strategies like canary or blue-green, approval gates, and rollback as built-in features.
Prerequisites: Cloud Run、GKE Enterprise (Anthos)、Deployment strategies (canary/blue-green/rollback)
Related: Cloud Build
Feature engineering and embeddings/RAG
Feature engineering shapes data for training/serving. For generative AI, turn unstructured data into embeddings and use retrieval-augmented generation (RAG) to fetch context for answers.
Prerequisites: RAG (retrieval-augmented generation)
BigQuery ML model types
Models you can train/predict in SQL when data is in BigQuery: classification/regression, ARIMA_PLUS for time-series, matrix factorization for recommendations, boosted trees (XGBoost) for high-accuracy tabular, autoencoders for anomaly detection—chosen by problem.
Prerequisites: BigQuery、BigQuery ML
Distributed training (Reduction Server/Horovod) and hyperparameter tuning
Means to make large-scale training efficient: Reduction Server offloading GPU data-parallel gradient aggregation (all-reduce) to dedicated nodes, framework-agnostic Horovod, and hyperparameter tuning (Vertex AI Vizier) to raise accuracy. Choose accelerators by workload (large TF/JAX=TPU, general=GPU, on-device=edge).
Prerequisites: Vertex AI
Vertex AI Model Monitoring (skew/drift)
Continuously evaluates production models and alerts on training/serving skew (training vs serving input distribution) and feature/prediction drift (change over time), triggering retraining on threshold breaches; use attribution change as a proxy when labels are delayed.
Prerequisites: Vertex AI
Vertex AI public/private endpoints
Deployment targets for online inference: public endpoints for internet exposure, private endpoints (Private Service Connect) to stay in-VPC with low latency. Follow demand with Vertex AI Prediction autoscaling or containerized serving.
Prerequisites: Vertex AI、Private Service Connect
Vertex AI Feature Store
Consolidates computed features for reuse across teams/models; uses the same features for training/serving to prevent duplicate computation and training/serving skew. Online serving for low-latency inference; offline for bulk training.
Prerequisites: Vertex AI、Vertex AI Model Monitoring (skew/drift)
Vertex AI Pipelines (Kubeflow/TFX)
A service that runs ML pipelines defined in Kubeflow Pipelines or TensorFlow Extended (TFX) managed and serverless; for general (incl. non-ML) workflow dependencies/scheduling use Airflow-based Cloud Composer.
Prerequisites: Cloud Composer、Vertex AI
A/B testing (Vertex AI)
Deploying multiple model versions to the same endpoint and specifying a traffic-split ratio to compare performance on real requests. If metrics degrade, traffic can be rolled back to the prior version quickly, letting you validate a new model while limiting production impact.
Prerequisites: Vertex AI、Deployment strategies (canary/blue-green/rollback)
Dataflow
A data-processing pipeline service supporting both batch and streaming.
Google Kubernetes Engine (GKE)
Managed Kubernetes that operates many containers at scale with automated placement, scaling, and self-healing.
Prerequisites: ML data preparation (encoding/scaling/imbalance)
Spanner
A distributed relational database combining global strong consistency and high availability, ideal for global core systems.
Colab Enterprise
The familiar Colab notebook experience integrated with Google Cloud's IAM, VPC, and data governance for enterprise use. Unlike personal Colab, compute runs inside the customer's own Google Cloud project, making team collaboration and access control easier. Well suited to ease-of-use-focused collaborative work.
Prerequisites: Data governance
API Gateway (Google Cloud)
A fully managed API gateway in front of serverless backends (Cloud Run/Functions/App Engine); defined with OpenAPI to handle auth, keys, and monitoring.
Prerequisites: App Engine、Cloud Run、Managed services (management boundary)
Cloud Interconnect
A dedicated-connection service (Dedicated/Partner) linking on-premises to Google Cloud with high bandwidth, low latency, and an SLA; choose vs the simpler encrypted-tunnel Cloud VPN by requirements.
Prerequisites: Cloud VPN
Private Google Access
A setting that lets VMs without external IPs reach Google APIs (e.g., Cloud Storage) privately; distinct in purpose from general egress via Cloud NAT.
Prerequisites: Cloud NAT、Cloud Storage
Cloud CLI emulators
Local mocks (Firestore, Pub/Sub, Spanner, etc.) provided by the Google Cloud CLI to speed local development/unit testing without connecting to the cloud (not a production substitute).
Three observability pillars (metrics, logs, traces)
Metrics (Cloud Monitoring, numeric trends), logs (Cloud Logging, events), and traces (Cloud Trace, request paths); correlate logs and traces by trace ID to pinpoint causes.
Related: Cloud Trace、Cloud Logging、Cloud Monitoring
Database Migration Service (Google Cloud DMS)
Google's managed service to migrate MySQL/PostgreSQL and others to Cloud SQL or AlloyDB with continuous replication, supporting minimal-downtime cutover.
Prerequisites: AlloyDB、Cloud SQL、Managed services (management boundary)
Private Service Connect
A way to reach specific managed/published services over private IPs without traversing the internet; use it to avoid exposing public IPs.
Prerequisites: Managed services (management boundary)
Cloud Router
A router that exchanges routes dynamically over BGP with on-prem/other networks; configure ASN, route priority/MED, and authentication, and control advertised/learned ranges with custom-advertised/learned routes.
Prerequisites: Route priority
Vertex AI Model Registry
A repository that centrally tracks trained models' versions, lineage, evaluation metrics, and deployment status. Multiple versions are grouped under the same model name, making it easy to see at a glance which version is deployed to which endpoint. It anchors model governance and reproducibility.
Prerequisites: Vertex AI
Vertex AI Experiments and TensorBoard
Tracking for experiment reproducibility: Vertex AI Experiments records/compares params/metrics/artifacts; Vertex AI TensorBoard visualizes learning curves. Complementary—use both.
Prerequisites: Vertex AI
RAG (retrieval-augmented generation)
Searching documents and adding them to the prompt for grounded answers; reduces hallucination.
YAML
A human-friendly text format that expresses hierarchy through indentation. Favored for CloudFormation and other IaC tools and CI/CD pipeline config files, and interconvertible with JSON representing the same data.
Prerequisites: JSON
Cloud DNS
A Google Cloud service that manages internal/external name resolution (domain → IP).
Cloud NAT
A service that lets VMs without external IPs make outbound calls to the internet.
Cloud VPN
A service connecting on-premises or other networks to Google Cloud over an encrypted tunnel.
GKE Autopilot
A GKE cluster mode where Google fully manages node provisioning, scaling, upgrades, and hardening. Billing is per-Pod, and you lose node-pool tuning and SSH access in exchange for minimal operational overhead.
Prerequisites: Managed services (management boundary)、ML data preparation (encoding/scaling/imbalance)
Managed instance group (MIG) and instance template
A Compute Engine group that creates identical VMs from an instance template (a VM blueprint), with autoscaling and self-healing.
Prerequisites: Compute Engine
Storage Transfer Service
A managed service to transfer large or continuous data into Cloud Storage; for small data, the gcloud/bq CLI is simpler.
Prerequisites: Cloud Storage、Managed services (management boundary)
Cloud KMS
A managed key-management service to create and manage encryption keys and control rotation/disabling; the basis of CMEK.
Related: Encryption keys (GMEK / CMEK / CSEK)
Lifecycle rules
Cloud Storage rules to automate "delete after N days" or "move to a cheaper class after N days," optimizing cost; in BigQuery, expiration plays a similar role.
Prerequisites: BigQuery、Cloud Storage
Scheduled queries
A simple BigQuery feature to auto-run the same query (e.g., aggregation) on a schedule; complex dependencies use Cloud Composer.
Prerequisites: Cloud Composer、BigQuery
App Engine
A fully managed web app platform (PaaS) to publish apps without managing servers, letting you focus on development.
Prerequisites: Managed services (management boundary)
Cloud Logging
A Google Cloud Observability capability for collecting, searching, and retaining logs, used for investigation and audit.
Related: Three observability pillars (metrics, logs, traces)
Cloud Monitoring
A Google Cloud Observability capability for metrics monitoring, dashboards, and alerts.
Related: Three observability pillars (metrics, logs, traces)
Conversational Agents
A solution to build conversational agents/chatbots that converse in natural language, automating tasks like customer support.
Prerequisites: Agent (agentic AI)
Looker
A business intelligence (BI) tool that visualizes analytics in dashboards to support business use.
Pub/Sub
A messaging service that ingests events in real time and distributes them to multiple services; used for streaming ingestion.
Vertex AI Agent Builder
A Vertex AI means to build agents that act autonomously toward a goal, and search apps.
Prerequisites: Vertex AI、Agent (agentic AI)
Cloud Billing
Links a billing account to projects to track costs, set budget alerts, and export billing data to BigQuery.
Prerequisites: Billing account and budgets、BigQuery
Deployment Manager
Google Cloud's native Infrastructure as Code service (YAML/Jinja/Python templates). It is deprecated and reaches end of support on 2026-03-31; migrate to the Terraform-based Infrastructure Manager or another IaC tool.
Prerequisites: Infrastructure Manager、YAML
Filestore
A fully managed NFS file storage that mounts as a shared file system from Compute Engine VMs or GKE, providing low-latency shared storage.
Prerequisites: Compute Engine、Managed services (management boundary)
VPC firewall rules
Stateful rules that allow/deny traffic in/out of a VPC, evaluated by direction, priority, target (tags/service accounts), and source; hierarchical firewall policies exist at the org level.
Prerequisites: Service account、Cloud NGFW and hierarchical firewall
Gemma
Google's family of lightweight open models derived from the same research as Gemini, suited for on-device or self-hosted use and fine-tuning.
Prerequisites: Gemini、Fine-tuning
Gemini Cloud Assist
An AI assistant inside the Google Cloud console that analyzes logs, metrics, traces, and resource configuration to offer natural-language troubleshooting and architecture suggestions. Aimed at cutting the time operators spend investigating root causes across the console.
Prerequisites: Gemini
Related: Gemini Code Assist
Gemini Code Assist
An AI pair-programming feature that assists with code completion, generation, explanation, and review inside an IDE. It has a free individual tier and an Enterprise tier that can ground completions in a private codebase and add enterprise administration. Used as an extension for VS Code and JetBrains-family IDEs.
Prerequisites: Gemini
Related: Gemini Cloud Assist
JSON
A lightweight text format representing structured data as braces and key–value pairs. Widely used wherever machine-readability matters most—API request/response bodies and permission definitions like IAM or bucket policies.
Linear regression
A foundational supervised-learning model that predicts a continuous target as a weighted linear combination of features. It's easy to interpret and is often the first baseline tried for a regression problem.
Logistic regression
A model that passes a linear combination of features through a sigmoid function to output a probability between 0 and 1, performing binary classification. Despite the name, it's a classifier—and like linear regression, it's interpretable and commonly used as a classification baseline.
Prerequisites: Linear regression
Cloud External Key Manager (Cloud EKM)
Encrypts Google Cloud data using keys held in an external (own/third-party) key-management backend, raising key sovereignty beyond CMEK (customer-managed via Cloud KMS).
Prerequisites: Cloud KMS、Encryption keys (GMEK / CMEK / CSEK)
Cloud Run revisions and traffic splitting
Each deploy creates an immutable revision; traffic splitting controls the percentage sent to each revision, enabling canary release and rollback.
Prerequisites: Cloud Run、Deployment strategies (canary/blue-green/rollback)
Cloud CDN
A CDN enabled on an external Application LB backend that caches content at Google's edge; origins include MIG/Cloud Storage/Cloud Run/internet NEG. Drop stale content via cache invalidation on updates.
Prerequisites: Cloud Run、Cloud Storage
Cloud NGFW and hierarchical firewall
A next-generation firewall controlling in-VPC traffic; offers hierarchical policies inherited by org/folder, an effective policy evaluated by priority, and Enterprise-tier L7 inspection (IPS). Implement micro-segmentation with tags/service accounts.
Prerequisites: Service account
Cross-Cloud Interconnect
A service that links another public cloud to Google Cloud over a dedicated physical link without the internet; use it for multicloud connectivity when you need more bandwidth/lower latency than Cloud VPN.
Prerequisites: Cloud VPN、Cloud Interconnect
Network endpoint group (NEG)
A load balancer backend unit that groups container-native (GKE), serverless (e.g., Cloud Run), or internet endpoints; chosen vs managed instance groups (MIGs) by need.
Prerequisites: Managed instance group (MIG) and instance template、Cloud Run
VLAN attachment
The logical link between a Cloud Interconnect and a VPC (Cloud Router); after creating the physical Interconnect, a VLAN attachment carries traffic to the actual VPC.
Prerequisites: Cloud Interconnect、Cloud Router
BigLake
A storage engine that lets BigQuery uniformly access data (e.g., in Cloud Storage) without moving it, bridging the lake and warehouse.
Prerequisites: BigQuery、Cloud Storage
BigQuery Data Transfer Service
A managed service that schedules loads from SaaS or other data warehouses into BigQuery; distinct in purpose from Database Migration Service (DB migration).
Prerequisites: BigQuery、Managed services (management boundary)
AlloyDB
A high-performance, PostgreSQL-compatible managed database.
Billing account and budgets
The unit that pays costs; link projects to it (one account, many projects). Budgets and alerts only notify at thresholds and do not auto-stop spending.
Organization Policy
A mechanism that inherits and enforces constraints down the resource hierarchy for consistent org-wide rules (e.g., disallow creation outside certain regions); distinct from IAM grants.
Prerequisites: Resource hierarchy
Cloud Storage classes
Classes by access frequency: Standard (frequent)/Nearline (~monthly)/Coldline (~quarterly)/Archive (long-term rare). Less access = cheaper storage but pricier retrieval.
Prerequisites: Cloud Storage
VPC Network Peering
Directly connecting two VPC networks so they can communicate with each other.
Prerequisites: Virtual Private Cloud (VPC)
Analytics Hub
A service to securely share/subscribe to BigQuery datasets with other organizations without making copies.
Prerequisites: BigQuery
Cloud Data Fusion
A managed service to build ETL/ELT pipelines visually with little code.
Prerequisites: Managed services (management boundary)
Dataform
A service to develop and operate SQL-based transformations (ELT) inside BigQuery with dependencies and version control.
Prerequisites: BigQuery
Looker Studio
A visualization (BI) tool to easily create and share reports and dashboards.
Prerequisites: Looker
LookML
A modeling language in Looker to define data metrics and dimensions, keeping measures consistent.
Prerequisites: Looker
Transfer Appliance
An offline transfer service that ships large data on a physical device for ingestion into Cloud Storage; suits volumes where network transfer is impractical.
Prerequisites: Cloud Storage
Uniform bucket-level access
A recommended Cloud Storage setting that manages permissions centrally via IAM at the bucket level instead of per-object ACLs—simpler and safer.
Prerequisites: Cloud Storage
Amazon Managed Service for Prometheus (AMP)
A managed, Prometheus-compatible metrics ingestion/storage service. You query container-environment metrics (EKS/ECS, etc.) with PromQL while AWS handles scale and retention—commonly paired with Managed Grafana for visualization.
Prerequisites: Managed services (management boundary)
Backpropagation
A technique that propagates a neural network's output error backward from the output layer to the input layer, computing each weight's gradient along the way. Paired with gradient descent to update the weights, it's the core algorithm underlying deep-learning training.
Prerequisites: Gradient descent
Cloud Functions
A serverless functions service for event-driven small tasks, such as running when a file is uploaded.
Firestore
A scalable document NoSQL database for mobile and web app data.
GKE Enterprise (Anthos)
A platform to operate and manage containers consistently across on-premises and multiple clouds, enabling hybrid and multicloud.
Resource hierarchy
The Google Cloud hierarchy of Organization → Folder → Project → resources; the project is the basic billing/permission unit and policies inherit down the hierarchy.
Spot VM
A Compute Engine VM offered at a deep discount in exchange for possible preemption; used for cost optimization of interruption-tolerant batch work.
Prerequisites: Compute Engine
Decision tree
An interpretable model that repeatedly branches on feature thresholds and predicts at the leaf nodes. Used alone, it tends to overfit the training data and needs depth limits or pruning to control—but it works for both regression and classification.
Chirp
Google's generative model family for speech. Originally a multilingual speech recognition (transcription) model; the later Chirp 3 generation also powers HD voice synthesis (text-to-speech).
Prerequisites: Foundation model
Data governance
Control over which data may be given to AI and how to protect sensitive information; a prerequisite for safe generative AI.
Gemini for Google Cloud
Gemini that assists development and operations, such as code completion/explanation and cloud-ops troubleshooting.
Prerequisites: Gemini
Gemini for Google Workspace
A productivity solution embedding Gemini into Docs, Gmail, Sheets, Slides, and Meet to assist drafting, email, summarization, and meeting notes.
Prerequisites: Gemini
Vertex AI Studio
A Vertex AI capability to quickly prototype, compare, and test prompts before production.
Prerequisites: Vertex AI
Contact Center as a Service (CCaaS)
A general term for cloud-delivered contact center platforms (SaaS-based, multichannel, no on-prem hardware; e.g., Amazon Connect, Genesys Cloud, Five9). In the Google Cloud context this refers to Google's CCaaS offering within the Customer Engagement Suite, combined with Conversational Agents and Agent Assist to automate and assist customer service.
Prerequisites: Conversational Agents
Classic VPN
A legacy, single-tunnel IPSec VPN gateway with no SLA guarantee. It comes in route-based (dynamic) and policy-based (static traffic-selector) variants, with the latter kept mainly for compatibility with specific peer network devices. Google deprecates it in favor of HA VPN for new builds.
Prerequisites: HA VPN
Cloud Code
An extension built into IDEs like VS Code and IntelliJ. It helps author Kubernetes manifests and Dockerfiles, deploy to GKE/Cloud Run directly from your local machine, and live-debug on a remote cluster—all from within the editor.
Prerequisites: Cloud Run
Cloud Endpoints
An API management service that manages OpenAPI/gRPC APIs via the Extensible Service Proxy, adding auth, monitoring, and quotas.
Prerequisites: gRPC
HA VPN
Google's recommended IPSec VPN, providing a 99.99% availability SLA via two redundant tunnels (each with its own external IP). Used to connect to on-prem, other clouds, or other VPCs, and assumes dynamic routing via BGP. The default choice for new deployments.
Virtual Private Cloud (VPC)
Google Cloud's software-defined network. A global resource that holds regional subnets in one network; routes, firewalls, and peering control traffic.
Workload Identity Federation (GCP)
A mechanism that trusts an external identity provider outside Google Cloud—AWS, Azure, on-prem, or CI/CD like GitHub Actions—and exchanges its issued tokens for temporary Google Cloud credentials. Used for external system integrations where you want to avoid downloading and distributing service-account keys.
Prerequisites: Service account
Dataplane V2
GKE's eBPF-based networking data plane. It enforces Kubernetes NetworkPolicy (Pod-to-Pod L3/L4 traffic control) efficiently at the kernel level and also provides visibility via flow logs. It has become the default for new clusters on recent GKE versions and runs with less overhead than the older iptables-based implementation.
IP masquerade (GKE)
A mechanism that source-NATs a GKE Pod's outbound (egress) traffic to the node's IP. By default, non-masqueraded (untranslated) destinations are RFC 1918 private ranges and link-local addresses; the ip-masq-agent configuration lets you add or change the target CIDRs. Whether this agent is auto-deployed as a DaemonSet, and its default configuration, depends on the GKE version and cluster setup (e.g., Autopilot vs. Standard, whether Dataplane V2 is in use).
Prerequisites: Dataplane V2
GKE Standard
A GKE cluster mode where you configure node-pool machine types, scaling settings, and even SSH access to nodes yourself. More flexible than Autopilot, but node capacity management and some security patching become your responsibility.
Prerequisites: ML data preparation (encoding/scaling/imbalance)
Gradient boosting
An ensemble technique that sequentially adds weak decision trees, each one trained to reduce the errors left by the trees before it (flagship implementations: XGBoost, LightGBM). A strong default for tabular data, and offered as a built-in algorithm by many managed ML services.
Prerequisites: Decision tree
Gradient descent
An optimization algorithm that minimizes a loss function by repeatedly stepping the parameters opposite the loss gradient, with step size set by the learning rate. Variants include full-batch, per-example stochastic gradient descent (SGD), and mini-batch processing.
k-means clustering
A classic unsupervised method that partitions data into k clusters by iterating assign-to-nearest-centroid then update-centroids. Choose k via the elbow method/silhouette; sensitive to scale, so standardize first. Used for customer segmentation, etc.
Prerequisites: ML data preparation (encoding/scaling/imbalance)
One-hot encoding
Converting a categorical variable into 0/1 flag columns, one per category—used for unordered categories (color, region) instead of label encoding (integers, which impose a false order). It increases dimensionality, so for high cardinality consider alternatives (embeddings).
Prerequisites: ML data preparation (encoding/scaling/imbalance)
p-value
The probability, assuming the null hypothesis is true, of observing a result at least as extreme as the data actually seen. A smaller value is stronger evidence to reject the null hypothesis (statistical significance), and it's the standard decision criterion in hypothesis tests like A/B testing.
Prerequisites: A/B testing (Vertex AI)
Cloud Trace
A distributed-tracing service that follows request paths (spans) across services to find latency bottlenecks; a different observability axis from metrics (Monitoring) or logs (Logging).
Related: Three observability pillars (metrics, logs, traces)
Infrastructure Manager
A Google Cloud service that runs Terraform as managed IaC, making infrastructure reproducible and reviewable/auditable via CI/CD and PRs.
Migration strategy (lift-and-shift/improve-and-move/refactor)
Lift-and-shift = move as-is (fastest, least change); improve-and-move = slight optimization; refactor = rebuild cloud-native (most effort, biggest payoff). Choose by requirements.
Application Default Credentials (ADC)
The standard way an app resolves credentials to authenticate to Google Cloud; combine with service accounts or Workload Identity Federation to avoid hardcoded keys.
Prerequisites: Service account
Artifact Registry
A registry to store and manage container images and language packages; the destination for artifacts built by Cloud Build.
Related: Cloud Build
Cloud SQL Auth Proxy
A proxy for secure Cloud SQL connections using IAM auth and encryption without exposing a public IP; the recommended way for apps to connect to the DB.
Prerequisites: Cloud SQL
gRPC
A low-latency, typed (Protocol Buffers) RPC framework over HTTP/2, suited to internal service-to-service calls; choose REST for public web.
Memorystore
A fully managed in-memory cache compatible with Redis/Memcached; caches frequent reads to reduce latency. Not a durable store.
Prerequisites: Managed services (management boundary)
Secret Manager
A managed service to securely store, retrieve, and rotate secrets (API keys, passwords, certificates); avoid embedding them in code.
Prerequisites: Managed services (management boundary)
Signed URL
A URL granting time-limited, scoped access to a Cloud Storage object without making it public; different from making a whole bucket public.
Prerequisites: Cloud Storage
Maintenance windows
A time window during which planned maintenance (patching, minor version updates) runs on a managed database. Set it to low-impact hours to align planned downtime with availability requirements.
Prerequisites: Windowing and late data (streaming)
Reverse replication
A mechanism that writes back from the new environment to the old after a migration cutover, providing rollback insurance to safely fall back if problems arise.
Prerequisites: Deployment strategies (canary/blue-green/rollback)
Artifact Analysis
Scanning that detects known vulnerabilities in container images/packages. Distinct from Binary Authorization (deploy-only-if-signed); combine detection with enforcement.
Prerequisites: Binary Authorization
Synthetic monitors
Actively and periodically probe endpoints/workflows from the user perspective (external monitoring); pair with passive monitoring to catch issues proactively.
Prerequisites: Workflows
Cloud DNS routing policies and split-horizon
Advanced Cloud DNS resolution: geolocation (vary by source region) and failover (switch to backup on primary failure) routing policies, plus split-horizon DNS resolving the same name differently via public/private zones.
Prerequisites: Cloud DNS
Dynamic routing mode (global/regional)
A VPC setting that determines how far BGP routes learned by Cloud Router propagate: regional stays within one region; global propagates to all regions. Use global to span multiple regions.
Prerequisites: Cloud Router
Hybrid DNS (forwarding zones/inbound policy/DNS peering)
Configurations for bidirectional name resolution between on-prem and Cloud DNS: forwarding zones query on-prem from cloud, inbound server policy resolves cloud from on-prem, and DNS peering references a zone in another VPC.
Prerequisites: Cloud DNS
Route priority
A value that decides which route is chosen when multiple share a destination; a lower value wins. Combine with network tags to scope where it applies.
Secure Web Proxy
A proxy that allowlists egress by URL/SNI; unlike Cloud NAT (general internet egress), it controls "where egress is allowed" at the application layer.
Prerequisites: Cloud NAT
BI Engine and materialized views
BI Engine accelerates BigQuery analytics in memory; materialized views precompute common aggregations. Distinct means to speed dashboards (combinable).
Prerequisites: BigQuery
BigQuery Editions and reservations
A way to secure compute capacity for steady use at predictable cost; on-demand suits small or irregular usage.
Prerequisites: BigQuery
Partitioning and clustering (BigQuery)
Partitioning splits a table by e.g. date; clustering orders rows by frequently filtered columns. Both reduce scan volume to optimize BigQuery cost/performance.
Prerequisites: BigQuery
Windowing and late data (streaming)
In streaming, design windowing (time-based aggregation) and late-arriving data handling with watermarks and allowed lateness; batch processes accumulated data together.
Vertex Explainable AI
A Vertex AI feature that quantifies how much each feature contributed to an individual prediction (feature attribution). It uses methods like sampled Shapley for tabular data and integrated gradients for image data. Used for regulatory explainability, debugging, and bias detection.
Prerequisites: Vertex AI