Vertex AI Development & MLOps Services
InterCode builds end-to-end machine learning systems on Google Cloud's Vertex AI platform — from data ingestion and model training through deployment, monitoring, and retraining. We help teams on GCP consolidate their ML infrastructure onto a single managed platform that scales from prototype to production.
Google Cloud's End-to-End ML Platform
Vertex AI is Google Cloud's unified machine learning platform that covers every stage of the ML lifecycle: managed notebooks in Vertex Workbench, AutoML for no-code model training, custom training jobs on managed GPU clusters, a model registry, one-click deployment to Vertex AI Endpoints, a Feature Store for consistent feature serving, and Model Monitoring for drift detection in production. At InterCode, we design Vertex AI architectures that replace fragmented ML tooling with a coherent, observable platform. We build training pipelines using Kubeflow Pipelines on Vertex that version datasets, track hyperparameters, and run evaluation steps automatically before promoting a model to the registry. We deploy models to Vertex AI Endpoints with traffic splitting for A/B testing and configure Model Monitoring to alert when prediction distributions diverge from baseline. For teams that need AI search and retrieval, we build solutions with Vertex AI Agent Builder — formerly Dialogflow CX — connecting it to BigQuery datasets, Cloud Storage corpora, and enterprise knowledge bases. We also leverage BigQuery ML for analyst-facing model training that runs SQL queries against production data and feeds predictions back into dashboards. Foundation models via Vertex AI Model Garden — including Gemini, Llama, and select Claude deployments — give teams access to state-of-the-art generative AI within the same GCP project boundary.
What We Build With Vertex AI
We build complete ML pipelines on Vertex AI that take raw data in BigQuery, engineer features in Vertex Feature Store, train a custom model on managed GPUs, evaluate it automatically, and deploy to a Vertex Endpoint — all in a reproducible, auditable pipeline. We implement real-time recommendation systems where Vertex Endpoints serve predictions at low latency to mobile and web applications. For business teams without ML expertise, we configure AutoML pipelines for demand forecasting, churn prediction, and image classification that deliver production-grade models without custom training code. We build enterprise AI search applications using Vertex AI Agent Builder connected to internal document corpora and structured databases, and we wire BigQuery ML predictions directly into Looker dashboards for analyst-accessible ML.
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Custom AI
Build production-ready AI applications, LLM systems, and autonomous AI agents with InterCode. We are a specialist ai software development agency that has shipped 50+ AI products — from prototypes to enterprise-scale platforms.
Learn moreCloud deployment &
Set up scalable, secure, and cost-optimised cloud infrastructure for your application. InterCode provides cloud deployment services, devops services, and managed cloud services — so your engineering team can focus on product features, not infrastructure problems.
Learn moreMachine Learning Development for Real Impact
Turn your data into a competitive advantage with custom machine learning models. InterCode builds end-to-end ML solutions from data pipelines and model development through deployment and MLOps.
Learn moreData Engineering for Smarter Decisions
Your data is only as valuable as the infrastructure that moves and transforms it. InterCode builds reliable data pipelines, warehouses, and streaming architectures that turn raw data into the insights your business depends on.
Learn moreFrequently Asked Questions
All three are managed ML platforms with similar feature sets. Vertex AI is the strongest choice for teams already on GCP — it integrates tightly with BigQuery, Cloud Storage, and Google's foundation models via Model Garden. SageMaker has the most mature feature set and is the natural fit for AWS teams. Azure ML integrates with the Microsoft data stack and Azure OpenAI. The decision is usually driven by your primary cloud provider and existing data infrastructure.
Vertex AI makes sense when you need reproducible training pipelines, model versioning, managed deployment endpoints, or drift monitoring. Raw GCP services — Compute Engine with GPUs, Cloud Run for serving, Cloud Scheduler for retraining — are viable for simple one-off workloads but become difficult to maintain as you add more models and teams. We recommend Vertex AI for any production ML system that will run continuously.
Vertex AI charges separately for each component: training jobs are billed by machine type and duration, Endpoints by node hours, Feature Store by storage and read operations, and Model Monitoring by prediction volume. AutoML training is billed by node hours with a minimum. There is no flat platform fee — you pay only for what you use. Costs can be controlled by using preemptible training VMs and auto-scaling endpoint nodes.
AutoML is the right choice when your dataset fits common task types — tabular classification, regression, image classification, object detection, or text classification — and you do not need to control the model architecture. It produces competitive results with minimal code. Custom training is necessary when you have a bespoke model architecture, need specific frameworks or libraries, or require fine-grained control over the training loop.
Vertex AI Agent Builder is a managed service for building search and conversational AI applications on Google Cloud, formerly known as Dialogflow CX and Enterprise Search. It handles infrastructure, grounding, and retrieval out of the box but is tightly coupled to GCP. LangChain is an open-source framework that gives you more flexibility and portability but requires you to manage serving infrastructure. We recommend Agent Builder for GCP-native teams that want a managed path, and LangChain for complex agent logic or multi-cloud requirements.
Yes. We have migrated ML systems from SageMaker, Azure ML, and self-hosted Kubernetes clusters to Vertex AI. The migration involves containerising training code into Vertex-compatible custom training jobs, migrating feature pipelines to Vertex Feature Store, redeploying models to Vertex Endpoints, and replacing existing monitoring with Vertex Model Monitoring. We run old and new systems in parallel during validation to avoid production risk.
Build Your ML Platform on Vertex AI
Talk to our GCP engineers about consolidating your machine learning infrastructure on Vertex AI. We will design a scalable, observable ML platform that fits your team and data stack.
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