Migration Services
Amazon SageMaker to Vertex AI Migration Services
Move SageMaker machine learning and generative AI workloads to Vertex AI through a structured migration focused on scalable development, MLOps, and deployment.
Migration Services
Move SageMaker machine learning and generative AI workloads to Vertex AI through a structured migration focused on scalable development, MLOps, and deployment.
Why Vertex AI?
Use managed notebooks, custom training, AutoML, experiments, pipelines, and scalable compute within one AI environment.
Register, version, evaluate, govern, and deploy models through Vertex AI Model Registry.
Automate training and deployment using Vertex AI Pipelines, metadata, model evaluation, monitoring, and CI/CD integrations.
Access Gemini and selected open and third-party models for generative AI development, customization, evaluation, grounding, and deployment.
How We Migrate
Review SageMaker Studio environments, notebooks, training jobs, pipelines, models, features, endpoints, monitoring, security, and AWS dependencies.
Define the Vertex AI architecture for development, training, experiments, pipelines, registry, prediction, monitoring, networking, and governance.
Adapt notebooks, training code, containers, dependencies, datasets, and compute configurations for Vertex AI Workbench and managed training.
Move model artifacts, experiment metadata, pipeline logic, registry processes, endpoints, batch inference, and monitoring workflows.
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 MigrationWorkload and Dependency Discovery. We identify notebooks, frameworks, containers, datasets, features, models, pipelines, endpoints, and connected AWS services.
Capability-Based Target Mapping. We map each SageMaker workload to the appropriate Vertex AI, BigQuery, Dataflow, Dataproc, or 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 | Amazon SageMaker | Vertex AI |
|---|---|---|
| Platform approach | AWS platform for developing, training, deploying, and managing machine learning models | Google Cloud AI platform for predictive ML, generative AI, deployment, evaluation, and governance |
| Development environment | SageMaker Studio, JupyterLab, Code Editor, notebooks, and supported IDEs | Vertex AI Workbench, Colab Enterprise, managed notebooks, SDKs, and Google Cloud development tools |
| Model training | Managed training jobs, distributed training, built-in algorithms, and custom containers | Managed custom training, distributed training, hyperparameter tuning, AutoML, GPUs, and TPUs |
| AutoML | SageMaker Autopilot supports automated model development | Vertex AI AutoML supports managed model development for eligible data types |
| Experiment tracking | SageMaker Experiments and MLflow integrations | Vertex AI Experiments with integrated metadata, parameters, metrics, artifacts, and pipeline runs |
| Pipeline orchestration | SageMaker Pipelines automates ML development workflows | Vertex AI Pipelines creates reusable, managed, and portable ML workflows |
| Model registry | SageMaker Model Registry manages model groups, versions, approvals, and lifecycle stages | Vertex AI Model Registry centralizes custom, AutoML, and BigQuery ML models with versioning and deployment integration |
| Feature management | SageMaker Feature Store provides offline and online feature storage | Vertex AI Feature Store and BigQuery-centered feature architectures support training and online serving |
| Online inference | SageMaker real-time, serverless, asynchronous, and multi-model endpoints | Managed Vertex AI endpoints with autoscaling, traffic splitting, private connectivity, and custom containers |
| Batch inference | SageMaker Batch Transform supports offline prediction workloads | Vertex AI Batch Prediction runs managed inference against supported Google Cloud data sources |
| Model monitoring | SageMaker Model Monitor supports data and model-quality monitoring for supported deployments | Vertex AI Model Monitoring supports scheduled monitoring, feature drift, prediction drift, alerts, and registered models |
| Explainability | SageMaker Clarify supports bias detection and model explainability | Vertex Explainable AI and model evaluation capabilities support interpretation and assessment |
| Generative AI | Amazon Bedrock integrations, JumpStart models, and SageMaker-based model development | Gemini, Model Garden, tuning, evaluation, grounding, RAG, safety controls, and agent-development services |
| Foundation models | JumpStart provides pretrained and foundation-model options | Model Garden provides Google, open, and selected third-party models within Vertex AI |
| Model deployment automation | SageMaker Projects, Pipelines, Model Registry, and AWS CI/CD services | Vertex AI Pipelines, Model Registry, Cloud Build, Artifact Registry, and Google Cloud CI/CD integrations |
| Data integration | Closely integrated with Amazon S3, Redshift, Glue, Athena, and other AWS services | Closely integrated with BigQuery, Cloud Storage, Dataflow, Dataproc, Spanner, and Google Cloud databases |
| Governance | Uses IAM, Model Cards, lineage, registry controls, and AWS governance services | Uses IAM, Model Registry, ML Metadata, audit logs, organization policies, and Google Cloud governance controls |
| Compute | Managed CPU and GPU instances across training and inference options | Managed CPU, GPU, TPU, distributed training, and scalable online and batch prediction resources |
| Operations | Teams manage SageMaker domains, AWS integrations, resources, models, and deployment configurations | Google manages core AI service infrastructure while teams focus on models and AI applications |
| Best suited for | Teams operating machine learning primarily within AWS | Teams building predictive and generative AI solutions across Google Cloud |
FAQs
Codimite can migrate SageMaker notebooks, training code, model artifacts, containers, experiments, pipelines, feature workflows, registry metadata, endpoints, monitoring, and connected machine learning processes.
Not directly. Each SageMaker 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, runtime dependencies, data access, container 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 ML Metadata, and model versions and approvals can move to Vertex AI Model Registry.
Endpoints are redesigned using Vertex AI online or batch prediction. Feature workflows may move to Vertex AI Feature Store or BigQuery, while monitoring is rebuilt using Vertex AI Model Monitoring and Cloud Monitoring.
We compare feature inputs, evaluation metrics, predictions, latency, throughput, explainability, monitoring behavior, security, and business acceptance criteria before production cutover.
Assess your SageMaker models, pipelines, endpoints, features, and MLOps workflows to build a practical Vertex AI migration roadmap.
Start Your Migration
