GCP ML Engineer (Google Cloud Certifications) study plan
GCP ML Engineer · Give each week a clear focus.
8 weeks · About 7 hours per week
Scale prototypes into ML models
Compare Vertex AI custom training versus AutoML on a small tabular dataset and record the accuracy/latency/cost tradeoffs
Package a scikit-learn training script into a custom container and run it as a Vertex AI CustomJob
Configure a Vertex AI Experiment run that logs hyperparameters, metrics, and artifacts for at least three trials
Your checkpoint
A one-page tradeoff matrix plus a completed CustomJob run with logged experiment metrics viewable in the console
Scale prototypes into ML models
Run a Vertex AI Vizier hyperparameter tuning job over at least 10 trials and justify the search space you defined
Explain and apply distributed training strategies (data-parallel with reduction server) to a dataset too large for one worker
Evaluate model performance against a baseline using appropriate metrics and document the go/no-go decision
Your checkpoint
A tuning job summary chart with best-trial metrics and a written justification of search space, strategy choice, and promotion decision
Serve and scale models
Deploy a trained model to a Vertex AI Endpoint and compare dedicated public endpoint, private endpoint, and shared/VPC-scoped serving options
Configure autoscaling (min/max replicas, target utilization) and measure the effect on latency under a synthetic load test
Select an appropriate machine type and accelerator for a latency-sensitive versus cost-sensitive serving scenario and defend the choice
Your checkpoint
A load-test report showing p50/p95 latency and cost per 1,000 predictions at two different autoscaling configurations
Serve and scale models & Collaborate within and across teams to manage data and models
Implement batch prediction on a large input set and contrast its cost profile with online prediction
Register a model in Vertex AI Model Registry with versioning and aliases, and promote a version to a deployment alias
Apply an explanation configuration (feature attributions) to a deployed model and interpret the output for a stakeholder
Your checkpoint
A model registry with at least two versions and one alias, plus a batch prediction output and an attribution explanation for a single prediction
Automate and orchestrate ML pipelines
Author a Vertex AI Pipelines definition that chains data validation, training, evaluation, and conditional model deployment
Wire pipeline components to pass artifacts via metadata rather than hardcoded paths, and verify lineage in Vertex ML Metadata
Trigger the pipeline on a schedule using Cloud Scheduler plus a serverless component, and document failure/retry behavior
Your checkpoint
A runnable pipeline YAML that executes end-to-end with a conditional deployment step and a visible lineage graph in ML Metadata
Monitor AI solutions
Enable Vertex AI Model Monitoring on a deployed endpoint and configure skew and drift thresholds for the top features
Define a training-serving skew detection baseline and interpret a fired alert in a simulated drift scenario
Set up prediction logging to BigQuery and query it to compute a daily feature distribution report
Your checkpoint
A monitoring configuration with alert thresholds plus a BigQuery query that reproduces one drift metric and an interpretation memo
Collaborate within and across teams to manage data and models & Architect low-code AI solutions
Design a data and feature management approach using Vertex AI Feature Store, including entity types and feature freshness requirements
Establish a shared dataset and model governance pattern across two teams, specifying IAM roles, quotas, and cost-allocation labels
Build a low-code solution with Vertex AI Agent Builder or Gen AI App Builder that grounds responses in a document store
Your checkpoint
An architecture diagram and accompanying governance checklist covering feature store design, IAM boundaries, and the low-code solution's grounding source
Architect low-code AI solutions & Monitor AI solutions & Serve and scale models
Assemble a full reference architecture that connects pipeline automation, serving, and monitoring into one coherent design
Diagnose three failure scenarios (endpoint quota exhaustion, pipeline component timeout, monitoring drift alert) and write the remediation steps
Complete a timed mixed-domain practice set and review every missed item to root cause
Your checkpoint
A complete end-to-end reference architecture document with three written incident playbooks and a scored practice-set review log
Milestones
Week 2: Can train, tune, and justify a model on Vertex AI without notes, and explain when AutoML is the right call over custom training
Week 4: Can deploy, autoscale, version, and explain a model end-to-end, and state the cost and latency consequences of each serving choice
Week 6: Can build an automated pipeline and configure drift monitoring on the model it produces, then trace an alert back to a metric query
Week 8: Scores at least 80% on a timed mixed-domain practice set and can defend an end-to-end architecture covering all six domains