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AI TRAINING

Google Vertex AI for ML Engineering Teams

Build, deploy, and monitor production ML models end-to-end on Google Vertex AI.

Format
bootcamp
Duration
16–24h
Level
practitioner
Group size
6–16
Price / participant
€2K–€3K
Group price
€12K–€28K
Audience
ML engineers, data scientists, and MLOps practitioners already working within Google Cloud Platform
Prerequisites
Hands-on experience with Python and ML model training; familiarity with GCP basics (IAM, GCS, BigQuery); prior exposure to any ML framework (TensorFlow, PyTorch, or scikit-learn)

What it covers

This practitioner-level training takes GCP-centric ML engineers through the full Vertex AI platform: Workbench environments, Model Garden, Pipelines (Kubeflow-based), Feature Store, and model deployment patterns including online and batch serving. Participants work through hands-on labs covering real model lifecycle scenarios, from experiment tracking to monitoring drift in production. The course also covers when Vertex AI is the right tool versus open-source alternatives such as MLflow, Airflow, or self-managed Kubernetes stacks. Delivery combines instructor-led sessions with GCP sandbox environments and pre-built lab notebooks.

What you'll be able to do

  • Configure and launch a Vertex AI Workbench environment with experiment tracking integrated into a model training workflow
  • Build and run a multi-step ML pipeline using Vertex AI Pipelines and Kubeflow components
  • Register, version, and deploy a model to a Vertex AI online endpoint with autoscaling and traffic splitting
  • Set up Vertex AI Model Monitoring to detect feature skew and prediction drift in production
  • Evaluate trade-offs between Vertex AI managed services and open-source MLOps tooling for a given team context

Topics covered

  • Vertex AI Workbench: managed notebooks and experiment tracking
  • Model Garden: foundation models, fine-tuning, and deployment
  • Vertex AI Pipelines: building and orchestrating Kubeflow-based ML pipelines
  • Feature Store: creating, sharing, and serving features at scale
  • Online and batch prediction endpoints: deployment patterns and autoscaling
  • Model monitoring: drift detection, skew detection, and alerting
  • Vertex AI vs open-source stacks: MLflow, Airflow, self-managed Kubernetes
  • Cost optimisation and resource management on GCP

Delivery

Typically delivered over 2-3 consecutive days, either on-site or via virtual instructor-led sessions using Google Meet or Zoom. Each participant requires a GCP project with billing enabled (or a trainer-provisioned sandbox account). Labs account for approximately 60% of total time; lecture and discussion make up the remaining 40%. Pre-reading materials covering GCP fundamentals are distributed one week in advance. A shared Git repository with starter notebooks is provided on day one.

What makes it work

  • Bring a real internal use case or dataset to the training so lab exercises map directly to participants' actual work
  • Assign a GCP champion within the team who maintains sandbox environments and propagates best practices after the training
  • Agree on a team-wide pipeline template and Feature Store naming convention before scaling beyond the first Vertex AI project
  • Schedule a follow-up review session 4-6 weeks post-training to address blockers encountered in real deployments

Common mistakes

  • Treating Vertex AI Pipelines as a direct drop-in for existing Airflow DAGs without redesigning task granularity and artifact passing
  • Ignoring IAM and VPC Service Controls until late in the project, causing blocked deployments in security-conscious environments
  • Over-relying on AutoML endpoints for all use cases without understanding latency, cost, and customisation limitations
  • Skipping model monitoring setup post-deployment, leaving production drift undetected until model quality degrades noticeably

When NOT to take this

Teams that are not yet committed to GCP as their primary cloud provider — investing in deep Vertex AI expertise before cloud strategy is settled creates rework risk if the organisation later migrates to AWS SageMaker or Azure ML.

Providers to consider

Sources

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