Migration Services
SAS to Vertex AI Migration Services
Move SAS machine learning and statistical modeling workloads to Vertex AI through a structured migration focused on scalable development and deployment.
Migration Services
Move SAS machine learning and statistical modeling workloads to Vertex AI through a structured migration focused on scalable development and deployment.
Why Vertex AI?
Use Python, open-source frameworks, AutoML, managed notebooks, and custom training environments based on each use case.
Register, version, evaluate, approve, deploy, and govern models through Vertex AI Model Registry.
Build repeatable training and deployment workflows using Vertex AI Pipelines, Experiments, metadata, and monitoring.
Access Gemini, Model Garden, model evaluation, grounding, tuning, and agent-building services in the same platform.
How We Migrate
Review SAS models, procedures, programs, datasets, features, training workflows, scoring logic, reports, dependencies, and governance.
Define the Vertex AI architecture for data access, notebooks, training, experiments, pipelines, registry, endpoints, monitoring, and security.
Translate SAS procedures and modeling logic into Python, BigQuery ML, AutoML, or custom Vertex AI training workflows.
Rebuild experiment tracking, feature preparation, model registration, approvals, scoring, deployment, and monitoring processes.
Compare statistical results, predictions, performance, explainability, latency, and business outcomes before production transition.
Why Codimite?
Codimite combines Google Cloud architecture, data science, machine learning engineering, MLOps, and generative AI expertise.
Start Your MigrationSAS Model Discovery. We identify statistical procedures, transformations, variables, features, scoring code, assumptions, and dependent reporting processes.
Model-by-Model Target Mapping. We determine whether each workload belongs in BigQuery ML, AutoML, custom Vertex AI training, or another Google Cloud service.
Reproducible Model Redevelopment. We rebuild models with documented features, versioned code, repeatable training, evaluation, and model lineage.
Controlled Production Transition. We reduce deployment risk through benchmark testing, shadow predictions, staged endpoints, monitoring, and rollback planning.
Platform Comparison
Both platforms support advanced analytics and machine learning, but Vertex AI provides a managed, open-framework environment for modern MLOps and generative AI.
| Area | SAS Analytics | Vertex AI |
|---|---|---|
| Platform approach | Statistical analysis, data science, machine learning, and reporting within the SAS ecosystem | Managed AI platform for developing, training, deploying, evaluating, and governing models |
| Development languages | SAS language, statistical procedures, macros, and supported integrations | Python, open-source ML frameworks, AutoML, custom containers, APIs, and notebooks |
| Development environment | SAS Studio, Enterprise Guide, Model Studio, and other SAS tools | Vertex AI Workbench, Colab Enterprise, notebooks, SDKs, and managed training |
| Model training | SAS procedures and products running on SAS-managed compute | Managed custom training, distributed training, AutoML, hyperparameter tuning, GPUs, and TPUs |
| Statistical modeling | Extensive built-in statistical and econometric procedures | Open-source statistical libraries, custom training, AutoML, and BigQuery ML integration |
| Experiment tracking | Depends on SAS product, metadata, and model-management setup | Vertex AI Experiments tracks parameters, metrics, artifacts, and pipeline runs |
| Model registry | SAS model repositories and governance tools | Centralized Vertex AI Model Registry for versions, evaluation, approval, and deployment |
| Pipeline orchestration | SAS jobs, flows, scheduling, and model-management processes | Vertex AI Pipelines for automated and reusable machine learning workflows |
| Feature engineering | SAS DATA steps, procedures, feature tools, and analytical workflows | BigQuery, Dataflow, Vertex AI Feature Store, pipelines, and Python-based processing |
| Model deployment | SAS scoring services, batch scoring, or product-specific deployment | Managed online endpoints, batch prediction, autoscaling, custom containers, and private networking |
| Model monitoring | Depends on SAS model-management and monitoring products | Vertex AI Model Monitoring with logging, alerting, evaluation, and Cloud Monitoring integration |
| AutoML | Capabilities depend on the selected SAS analytics products | Vertex AI AutoML supports managed model development for eligible data types |
| Generative AI | Generative AI capabilities depend on the selected SAS environment and integrations | Gemini, Model Garden, tuning, evaluation, grounding, RAG, and agent-building services |
| Model choice | SAS algorithms and supported integrated frameworks | Google models plus selected open and third-party models through Model Garden |
| Data integration | Closely connected to SAS libraries and supported enterprise data sources | Closely integrated with BigQuery, Cloud Storage, Dataflow, Dataproc, and Google Cloud databases |
| Governance | Uses SAS metadata, roles, model controls, and product-specific governance | IAM, Model Registry, metadata, audit logging, organization policies, and Google Cloud governance |
| Compute | Depends on SAS servers, grid infrastructure, cloud deployment, and licensing | Managed CPU, GPU, TPU, distributed training, and scalable prediction resources |
| MLOps automation | Uses SAS scheduling, model management, and deployment processes | Vertex AI Pipelines, Cloud Build, Artifact Registry, Model Registry, and CI/CD integrations |
| Operations | Teams manage SAS environments, licenses, services, code, and model processes | Google manages core AI infrastructure while teams manage models and applications |
| Best suited for | Organizations with established SAS statistical and modeling workflows | Teams building managed machine learning and generative AI solutions on Google Cloud |
FAQs
Codimite can migrate statistical models, machine learning workflows, SAS programs, feature-engineering logic, training data, scoring code, schedules, reports, and model deployment processes.
No. Each SAS workload must be assessed and mapped to the most suitable Google Cloud service, such as Vertex AI, BigQuery ML, AutoML, Python libraries, or a custom training workflow.
Some model coefficients, rules, transformations, and exported artifacts may be reusable. However, many SAS models and scoring processes require redevelopment in Python, BigQuery ML, or a Vertex AI-compatible framework.
SAS procedures and feature logic are translated into suitable Python libraries, BigQuery SQL, Dataform, Dataflow, Vertex AI Pipelines, AutoML, or custom model implementations.
Models can be registered in Vertex AI Model Registry and deployed for online or batch predictions. Versioning, approvals, monitoring, and promotion workflows are rebuilt using managed MLOps services.
We compare features, coefficients, evaluation metrics, predictions, thresholds, confidence measures, latency, model behavior, and business acceptance criteria before production cutover.
Assess your SAS modeling and deployment workflows to build a practical roadmap for redevelopment, validation, MLOps, and production deployment.
Start Your Migration
