Navigating the Frontier of Enterprise AI: Key Services on Vertex AI – Part 2

Vertex AI is Google Clouds unified, fully-managed AI platform that brings everything you need to build and scale enterprise AI in one place. From generative AI to traditional ML, Vertex AI accelerates development with a single environment, powered by models like Gemini, and tools for training, tuning, and deploying at scale.

This blog is part two of our Vertex AI series. In the first part, we explored:

1. Core Services of Vertex AI: including Model Garden, Vertex AI Studio, GenAI Evaluation, and Tuning.

2. Vertex AI Agent Builder: covering Agent Garden, Vertex AI Agent Engine, RAG Engine, Vertex AI Search, and Vector Search.

3. Notebooks: featuring Colab Enterprise and Vertex AI Workbench.

Now, in this second part, we will dive deeper into:

4. Model Development: exploring key components such as Feature Store, Datasets, Training, Experiments, Metadata, and Ray on Vertex AI.

5. Deploy and Use: covering Model Registry, Online and Batch Inference, and Monitoring.

Through these sections, we will uncover how Vertex AI provides an end-to-end workflow that supports every stage of the machine learning lifecycle, from feature engineering and model training to deployment and continuous monitoring.

🎥 Prefer watching instead of reading? You can watch the NotebookLM podcast video with slides and visuals based on this blog here.

4. Model Development

Vertex AI does not just stop at providing a place to deploy models; it delivers an entire ecosystem for model lifecycle management, from feature engineering and dataset curation to training, experimentation, and scaling. This foundation is critical for organizations that want to move beyond proof-of-concepts into enterprise-grade AI. The following components illustrate how Vertex AI enables a production-first approach to machine learning, ensuring that every step is tracked, repeatable, and optimized for scale.

4.1 Feature Store

The Vertex AI Feature Store is at the heart of operational ML pipelines, solving one of the biggest challenges in AI: managing and reusing features consistently across training and serving.

• Integrated Storage with BigQuery: Rather than forcing teams to maintain separate infrastructure, Feature Store directly leverages BigQuery as the offline store, letting data scientists use historical data for training without duplication.
• Online Serving for Real-Time AI: For live predictions, the Feature Store ensures the latest feature values are available at low latencies.
  
  • Bigtable Serving: Suited for massive, high-throughput workloads, optimized for cache-heavy use cases.
  • Optimized Online Serving: Ideal for ultra-fast predictions with support for embeddings, critical for vector search, personalization engines, and recommendation systems.
• Feature Management at Scale: Teams can organize features into Feature Groups and track them with a Feature Registry, reducing duplication and making features reusable across projects.
• Monitoring and Drift Detection: With built-in feature statistics and anomaly detection, businesses can proactively address data drift, ensuring their models stay accurate over time.

💡 This means that whether your application is a fraud detection engine or a real-time recommender, the Feature Store helps keep features fresh, consistent, and production-ready.

🌟 To learn more about Vertex AI Feature Store, watch the video below:

4.2 Datasets

Datasets form the backbone of machine learning, and Vertex AI provides a managed dataset service that simplifies the process of organizing, labeling, and preparing data for model training.

• Centralized Management: All datasets are stored in one place, reducing silos and enabling easy collaboration. Labels, annotations, and lineage are natively tracked.
• Support for Multiple Data Types: From images and video to tabular and time-series data, Vertex AI datasets cover the majority of enterprise use cases. This flexibility means you can train everything from a demand forecasting model to a computer vision system within the same platform.
• Human-in-the-Loop Labeling: Vertex AI integrates human labeling services for scenarios where quality training data requires expert input (e.g., medical imaging).

💡 This structured dataset approach reduces overhead, accelerates experimentation, and ensures teams spend less time on plumbing and more time on insights.

🌟 To learn more about Vertex AI Datasets, watch the video below:

4.3 Training

Training is where ideas meet compute power, and Vertex AI caters to both beginners and advanced ML engineers.

• AutoML for Rapid Innovation: Ideal for teams with limited ML expertise, AutoML automates model training, tuning, and deployment. This is especially powerful for quick-turnaround business use cases such as churn prediction or customer segmentation.
• Custom Training for Control and Scale: For data science teams requiring fine-grained control, Vertex AI supports training on any ML framework, packaged as Docker containers.
  
  • Compute Flexibility: Configure everything from CPUs to GPUs and TPUs, with autoscaling options.
  • Job Types: Run custom jobs, automate hyperparameter tuning, or integrate training into a full pipeline that outputs models to the Model Registry.
• Seamless Integration with Pipelines: Training jobs connect directly into Vertex AI Pipelines for reproducibility and CI/CD in machine learning.

💡 This dual-path training approach balances speed and flexibility, ensuring that organizations can meet diverse ML needs without sacrificing productivity.

🌟 To learn more about Vertex AI Training, watch the video below:

4.4 Experiments

Experiments in Vertex AI provide the structure for iterative model development, enabling data scientists to compare approaches and select the best-performing model with confidence.

• Run Tracking: Every training attempt, whether AutoML or custom, is logged with details about datasets, hyperparameters, and metrics.
• Centralized Comparison: Results are stored in Vertex ML Metadata and visualized in Vertex AI TensorBoard, making it easy to compare runs side by side.
• Reproducibility: Experiments serve as a contextual grouping, ensuring that decisions can be traced back to data and configurations.

💡 By treating experiments as first-class citizens, Vertex AI ensures that ML is not just trial-and-error; it is a structured process where results can be justified, replicated, and scaled.

🌟 To learn more about Vertex AI Experiments, watch the video below:

4.5 Metadata

At enterprise scale, metadata tracking is more than a nice-to-have; it is essential for compliance, reproducibility, and governance.

• Comprehensive Graph Model: Artifacts (datasets, models), Executions (workflow steps), and Contexts (experiments, pipelines) form a rich metadata graph that allows for complex queries and deep insights.
• Lineage and Auditability: Teams can answer questions like:
  
  • Which dataset produced this model?
  • Which training job achieved the best results?
  • How has model performance changed with different hyperparameters?
• Debugging and Analysis: Metadata provides transparency into why a model succeeded or failed, so teams can iterate more effectively.

💡 This metadata-driven approach transforms ML from an experimental process into a controlled engineering discipline, aligning with enterprise IT and regulatory standards.

4.6 Ray on Vertex AI

Ray on Vertex AI (RoV) takes distributed AI workloads to the next level, combining the flexibility of open-source Ray with the scalability of Google Cloud.

• Native Ray Integration: Developers who already use Ray can seamlessly run their code in Vertex AI with minimal changes.
• Managed Clusters: Persistent Ray clusters reduce the friction of managing distributed resources, making large-scale parallel training or reinforcement learning easier to manage.
• Autoscaling and Resource Optimization: Ray worker pools expand and contract based on workload demand, ensuring efficiency.
• Integrated Development: Clusters can be accessed through Colab Enterprise or Vertex AI Workbench, with full SDK support for Ray.
• Data Integration with BigQuery: RoV natively connects to BigQuery, letting teams process large-scale tabular data without additional ETL pipelines.

💡 This makes Vertex AI not only a platform for traditional ML and deep learning but also a home for next-generation workloads like simulation, reinforcement learning, and distributed training at scale.

🌟 To learn more about Ray on Vertex AI, watch the video below:

5. Deploy and Use

Training a machine learning model is only half the battle, the true business impact comes when models are operationalized in production. This means ensuring models are accessible to applications and users at scale, while continuously monitoring and maintaining their quality. Vertex AI provides a comprehensive toolset to cover this final stage of the MLOps lifecycle: registration, deployment, inference, and monitoring.

5.1 Model Registry

The Vertex AI Model Registry acts as the single source of truth for all models within an enterprise. Whether models are developed using AutoML, custom training, or BigQuery ML, the registry standardizes how they are stored, versioned, and deployed.

• Centralized Management: A unified repository provides visibility across all projects, ensuring models do not live in isolated silos. Teams can quickly see which models are in production, in testing, or retired.
• Versioning for Safe Iteration: Each model can have multiple versions, making it possible to track the evolution of experiments, roll back to a previous release, or compare performance between iterations.
• Direct Integration: From the Registry, teams can evaluate models, launch batch predictions, or deploy them to endpoints for online inference. BigQuery ML models also register directly without additional artifact management, streamlining workflows for data teams.
• Aliases and Labels for Governance: Models can be tagged with aliases (e.g., production, staging) and labels, enabling enterprises to enforce governance policies and align model lifecycles with business processes.

💡 By centralizing and governing models in this way, organizations can ensure that only validated, approved models reach production, reducing risk while accelerating deployment speed.

5.2 Online Inference

Online inference powers the real-time predictions that drive customer-facing applications, such as fraud detection systems, recommendation engines, and conversational AI.

• Endpoints as Gateways: To enable real-time predictions, models are deployed to an endpoint. This endpoint provides a URL where applications send prediction requests.
  
  • Multiple models can be deployed to one endpoint, allowing gradual rollout through traffic splitting (e.g., shifting 10% of traffic to a new version while monitoring performance).
  • Endpoints can be public or private, with best practices recommending dedicated endpoints using Private Service Connect for security-sensitive workloads.
• Scalability and Efficiency: Endpoints automatically scale up or down based on demand, with resource utilization thresholds configurable by the user. Importantly, scaling configurations can be modified without redeployment, ensuring operational agility.
• Flexible Request Types:
  
  • predict for standard inference.
  • explain for interpretability, surfacing feature attributions that explain why the model made a prediction.
  • rawPredict for advanced custom containers requiring lower latency and custom payloads.

💡 This makes online inference a highly flexible deployment option, supporting everything from low-latency transactional systems to interpretability-driven enterprise use cases.

5.3 Batch Inference

Not all workloads require instant responses. Batch inference allows enterprises to run large-scale, asynchronous prediction jobs that process millions of records in one go.

• Input and Output Flexibility:
  
  • Inputs can come from BigQuery tables, Cloud Storage CSV/JSONL files, or TFRecords, depending on the model type.
  • Outputs can be written back to BigQuery for analytics-ready storage or to Cloud Storage in CSV/JSONL format.
• Optimized Resource Allocation: Unlike online inference, batch jobs do not autoscale. Instead, you configure replica counts upfront for predictable cost and runtime. Best practices suggest right-sizing machines to ensure efficiency over long-running jobs.

💡 Batch inference is a natural fit for data lake scoring pipelines, customer segmentation analyses, or periodic compliance reporting, where throughput matters more than latency.

🌟 To learn more about Model Registry, Online Inference and Batch Inference on Vertex AI, watch the video below:

5.4 Monitoring

Deploying a model is not the finish line; it is the start of ongoing performance management. Vertex AI provides monitoring solutions that keep models healthy, accurate, and accountable.

• Model Monitoring v1:
  
  • Configured at the endpoint level.
  • Detects skew (difference between training and serving data) and drift (changes in production data distributions over time).
  • Works for both categorical and numerical features, using distance metrics like Jensen-Shannon divergence.
• Observability Dashboard for MaaS Models: For managed models like Gemini or partner LLMs, Vertex AI includes a purpose-built dashboard with insights into latency, throughput, and error rates, critical for large-scale GenAI applications.
• Cloud Monitoring Integration: Enterprises can view metrics such as prediction counts, latency, and errors in Metrics Explorer, set up custom dashboards, and configure alerts to integrate directly into incident response workflows.

💡 By embedding monitoring into the lifecycle, Vertex AI ensures that organizations are not just deploying models but also sustaining their performance over time, catching issues before they impact business outcomes.

⭐⭐⭐

Building, training, and deploying machine learning models at enterprise scale requires robust infrastructure and streamlined workflows, exactly what Vertex AI delivers. From Feature Store and managed datasets that simplify data preparation, to training pipelines and experiments that accelerate model development, Vertex AI ensures that data scientists and ML engineers can focus on creating high-quality models without getting bogged down in infrastructure challenges.

The platform also provides comprehensive MLOps capabilities. With the Model Registry, organizations can govern models, track versions, and maintain compliance. Online and batch inference options make it easy to deliver predictions in real time or at scale, while monitoring tools track model health, detect drift, and maintain performance over time. Services like Ray on Vertex AI add additional flexibility for distributed and parallel workloads, ensuring that even complex AI projects run efficiently.

By combining feature management, scalable training, experiment tracking, deployment, and monitoring, Vertex AI transforms machine learning from isolated experiments into production-grade, enterprise-ready systems.

Take the next step in your AI journey and contact us today to leverage Vertex AI to build, deploy, and maintain models that drive real business impact.