Migration Services
Azure Machine Learning to Vertex AI Migration Services
Move Azure Machine Learning models, pipelines, endpoints, and MLOps workflows to Vertex AI through a structured migration focused on scalable development and deployment.
Migration Services
Move Azure Machine Learning models, pipelines, endpoints, and MLOps workflows to Vertex AI through a structured migration focused on scalable development and deployment.
Why Vertex AI?
Use managed notebooks, custom training, AutoML, experiments, pipelines, and scalable compute within one platform.
Register, version, evaluate, deploy, and govern models through Vertex AI Model Registry.
Automate training and deployment workflows with Vertex AI Pipelines, metadata, evaluation, monitoring, and CI/CD integrations.
Access Gemini and selected open and third-party models for generative AI development, tuning, grounding, evaluation, and deployment.
How We Migrate
Review Azure ML workspaces, notebooks, jobs, pipelines, models, environments, features, endpoints, monitoring, security, and Azure dependencies.
Define the Vertex AI architecture for development, training, experiments, pipelines, registry, prediction, monitoring, networking, and governance.
Adapt notebooks, training code, environments, containers, datasets, and compute settings for Vertex AI Workbench and managed training.
Move model artifacts, registry metadata, components, pipeline logic, endpoints, batch inference, feature workflows, and monitoring processes.
Compare predictions, metrics, latency, throughput, security, monitoring, and business outcomes before transitioning production workloads.
Why Codimite?
Codimite combines Google Cloud architecture, data science, machine learning engineering, MLOps, and generative AI expertise.
Start Your MigrationWorkspace and Asset Discovery. We identify models, environments, components, datasets, pipelines, endpoints, features, permissions, and connected Azure services.
Capability-Based Target Mapping. We map Azure ML capabilities to Vertex AI, BigQuery, Dataflow, Dataproc, Cloud Storage, or another supporting Google Cloud service.
Reproducible MLOps Migration. We rebuild training and deployment workflows with versioned components, tracked metadata, automated evaluation, and controlled releases.
Controlled Production Transition. We reduce risk through benchmark testing, shadow predictions, parallel inference, staged traffic, monitoring, and rollback planning.
Platform Comparison
Both platforms support end-to-end machine learning, but Vertex AI provides a deeply integrated Google Cloud environment for predictive and generative AI.
| Area | Azure Machine Learning | Vertex AI |
|---|---|---|
| Platform approach | Azure platform for building, training, deploying, and managing machine learning models | Google Cloud AI platform for predictive ML, generative AI, deployment, evaluation, and governance |
| Development environment | Azure ML Studio, notebooks, compute instances, VS Code, and SDK tools | Vertex AI Workbench, Colab Enterprise, managed notebooks, SDKs, and Google Cloud development tools |
| Model training | Managed jobs, compute clusters, automated ML, distributed training, and custom environments | Managed custom training, distributed training, hyperparameter tuning, AutoML, GPUs, and TPUs |
| AutoML | Automated ML supports selected classification, regression, forecasting, vision, and NLP use cases | Vertex AI AutoML supports managed model development for eligible data types |
| Experiment tracking | Azure ML jobs, MLflow tracking, metrics, artifacts, and workspace history | Vertex AI Experiments tracks parameters, metrics, artifacts, metadata, and pipeline runs |
| Pipeline orchestration | Azure ML pipelines use reusable components and managed jobs | Vertex AI Pipelines provides managed, repeatable, and portable ML workflows |
| Model registry | Azure ML registries and workspace models manage versions, environments, components, and promotion | Vertex AI Model Registry centralizes custom, AutoML, externally trained, and BigQuery ML models |
| Feature management | Azure ML managed feature store supports feature discovery, materialization, reuse, and serving | Vertex AI Feature Store and BigQuery-centered architectures support feature management and online serving |
| Online inference | Managed online endpoints support real-time inference, traffic routing, mirroring, and autoscaling | Managed Vertex AI endpoints support autoscaling, traffic splitting, private connectivity, and custom containers |
| Batch inference | Batch endpoints support asynchronous inference over large datasets and reusable components | Vertex AI Batch Prediction provides managed offline inference against supported Google Cloud data sources |
| Model monitoring | Azure Monitor, endpoint metrics, Application Insights, and model-monitoring capabilities | Vertex AI Model Monitoring supports scheduled monitoring, drift detection, alerts, and Cloud Monitoring integration |
| Explainability | Azure ML Responsible AI tools support model interpretation, error analysis, and fairness assessment | Vertex Explainable AI and model evaluation capabilities support interpretation and responsible AI workflows |
| Generative AI | Azure AI Foundry and Azure OpenAI can complement Azure ML for generative AI workloads | Gemini, Model Garden, tuning, evaluation, grounding, RAG, safety controls, and agent-development services |
| Foundation models | Model catalog and Azure AI services provide Microsoft, OpenAI, and selected partner models | Model Garden provides Google, open, and selected third-party models within Vertex AI |
| Container support | Supports curated environments, custom Docker images, MLflow, and Kubernetes deployment options | Supports prebuilt and custom containers for managed training and prediction |
| Data integration | Closely integrated with Azure Blob Storage, Data Lake Storage, Synapse, Fabric, and Azure databases | Closely integrated with BigQuery, Cloud Storage, Dataflow, Dataproc, Spanner, and Google Cloud databases |
| Governance | Uses Microsoft Entra ID, Azure RBAC, registries, lineage, policies, and workspace controls | Uses IAM, Model Registry, ML Metadata, audit logs, organization policies, and Google Cloud governance controls |
| Compute | Uses managed CPU and GPU compute instances, clusters, Kubernetes, and serverless options | Provides managed CPU, GPU, TPU, distributed training, and scalable prediction resources |
| Operations | Teams manage workspaces, compute, environments, Azure integrations, models, and deployment settings | Google manages core AI service infrastructure while teams focus on models and applications |
| Best suited for | Teams operating machine learning primarily within Microsoft Azure | Teams building predictive and generative AI solutions across Google Cloud |
FAQs
Codimite can migrate Azure ML notebooks, training code, environments, components, models, experiments, pipelines, feature workflows, online and batch endpoints, monitoring, and connected machine learning processes.
Not directly. Each Azure ML capability must be assessed and mapped to the most suitable Vertex AI or supporting Google Cloud service based on the workload, architecture, and operational requirements.
Often yes. Reuse depends on the model framework, serialization format, Conda or Docker dependencies, data access, runtime configuration, and Vertex AI training or prediction requirements.
Pipeline logic can be rebuilt using Vertex AI Pipelines, experiment data can be mapped to Vertex AI Experiments, and model versions, metadata, and promotion processes can move to Vertex AI Model Registry.
Online and batch endpoints are redesigned using Vertex AI prediction services. Feature workflows may move to Vertex AI Feature Store or BigQuery, while dependencies on Azure Storage, Synapse, Key Vault, and other services are mapped to suitable Google Cloud services.
We compare feature inputs, evaluation metrics, predictions, latency, throughput, explainability, monitoring behavior, security, and business acceptance criteria before production cutover.
Assess your Azure ML models, pipelines, endpoints, features, and dependencies to build a practical Vertex AI migration roadmap.
Start Your Migration
