Migration Services
MLflow to Vertex AI Migration Services
Move MLflow experiments, models, artifacts, registries, and deployment workflows to Vertex AI through a structured migration focused on managed MLOps and governance.
Migration Services
Move MLflow experiments, models, artifacts, registries, and deployment workflows to Vertex AI through a structured migration focused on managed MLOps and governance.
Why Vertex AI?
Track model parameters, metrics, artifacts, pipeline runs, and lineage without operating a separate MLflow tracking server.
Register, version, evaluate, govern, and deploy models using Vertex AI Model Registry.
Automate training, evaluation, registration, approval, and deployment through managed Vertex AI Pipelines.
Deploy models to managed endpoints and connect them with evaluation, batch prediction, logging, and model monitoring.
How We Migrate
Review MLflow experiments, runs, metrics, parameters, artifacts, registered models, aliases, tags, deployments, storage, and dependencies.
Define the Vertex AI architecture for Experiments, ML Metadata, Model Registry, Pipelines, training, endpoints, monitoring, and governance.
Transfer required run metadata, metrics, parameters, datasets, model artifacts, and lineage into the appropriate Vertex AI and Google Cloud services.
Import compatible models, rebuild registry processes, and redesign training, evaluation, approval, deployment, and rollback workflows.
Compare models, metadata, predictions, lineage, pipeline behavior, endpoint performance, and governance before retiring approved MLflow services.
Why Codimite?
Codimite combines Google Cloud architecture, machine learning engineering, model migration, pipeline automation, and production governance expertise.
Start Your MigrationComplete MLflow Discovery. We identify experiments, registered models, aliases, artifacts, tracking stores, serving targets, plugins, and connected infrastructure.
Capability-Based Mapping. We map MLflow tracking, registry, evaluation, packaging, and deployment functions to the appropriate Vertex AI services.
Model and Metadata Preservation. We retain essential model versions, metrics, parameters, artifacts, descriptions, approvals, and lineage where technically practical.
Controlled MLOps Transition. We reduce risk through parallel tracking, model comparison, staged endpoint deployment, pipeline validation, and rollback planning.
Platform Comparison
MLflow provides portable open-source ML lifecycle tools, while Vertex AI delivers a broader managed platform for development, training, deployment, and governance.
| Area | MLflow | Vertex AI |
|---|---|---|
| Platform approach | Open-source tools for tracking, evaluation, model packaging, registry, and deployment | Managed Google Cloud platform for developing, training, deploying, evaluating, and governing AI models |
| Infrastructure | Teams operate or obtain hosting for tracking servers, backend stores, artifact stores, and serving targets | Google manages the core platform infrastructure for experiments, training, pipelines, registry, and prediction |
| Experiment tracking | MLflow Tracking logs runs, parameters, metrics, artifacts, datasets, and code information | Vertex AI Experiments tracks parameters, metrics, artifacts, training runs, and pipeline runs |
| Metadata and lineage | Run and model lineage is maintained through MLflow Tracking and Model Registry | Vertex ML Metadata tracks artifacts, executions, contexts, relationships, and downstream lineage |
| Tracking server | Uses local files or a self-hosted or managed MLflow Tracking Server | Managed Vertex AI services remove the need to operate a separate tracking server |
| Backend storage | Requires a filesystem or database-backed backend and a configured artifact store | Metadata and managed service resources are stored and operated within Google Cloud services |
| Model packaging | MLflow Models package artifacts, dependencies, signatures, and supported model flavors | Custom-trained models can be imported with prebuilt or custom prediction containers |
| Model registry | Provides registered models, versions, aliases, tags, descriptions, and lineage | Vertex AI Model Registry centralizes model versions, metadata, evaluation, deployment, and governance |
| Model aliases | Mutable aliases can point to selected registered model versions | Model versions and deployment workflows are managed through registry resources, aliases, labels, and automation patterns |
| Experiment comparison | MLflow UI compares runs, parameters, metrics, and artifacts | Vertex AI Experiments supports run comparison and integrates with TensorBoard and ML Metadata |
| Pipeline orchestration | Commonly integrates with external orchestrators or platform-specific workflow tools | Vertex AI Pipelines runs managed ML workflows defined with Kubeflow Pipelines or TFX |
| Training | Training is executed through external compute environments and logged to MLflow | Managed custom training, distributed training, hyperparameter tuning, AutoML, GPUs, and TPUs |
| Online deployment | Supports local serving, containers, plugins, cloud targets, and Kubernetes-based deployment | Managed Vertex AI endpoints support autoscaling, traffic splitting, private access, and custom containers |
| Batch inference | Requires a compatible MLflow deployment target or custom batch architecture | Vertex AI Batch Prediction provides managed offline inference |
| Model evaluation | MLflow supports model evaluation, metrics, validation, and comparison workflows | Vertex AI provides model evaluation integrated with registry, experiments, pipelines, and deployment workflows |
| Monitoring | Production monitoring depends on MLflow capabilities and connected observability systems | Vertex AI Model Monitoring integrates with Cloud Logging, Cloud Monitoring, drift detection, and alerts |
| Framework support | Supports multiple model flavors and Python-function models | Supports common ML frameworks through prebuilt containers and custom frameworks through custom containers |
| Container management | Teams build, store, scan, deploy, and operate serving containers | Images are stored in Artifact Registry and deployed through managed Vertex AI training or prediction |
| Access control | Depends on the MLflow deployment, hosting platform, proxy, and backend configuration | Google Cloud IAM, service accounts, private networking, audit logs, encryption, and organization policies |
| Generative AI | Provides tools for LLM tracing, evaluation, prompt management, and model access | Provides Gemini, Model Garden, tuning, grounding, evaluation, RAG, and agent-development capabilities |
| Cloud integration | Portable across local, cloud, and third-party environments | Closely integrated with BigQuery, Cloud Storage, Dataflow, Dataproc, Gemini, and Google Cloud services |
| Operations | Teams manage servers, databases, artifact storage, upgrades, scaling, security, and availability | Google manages core infrastructure while teams focus on models, pipelines, and AI applications |
| Best suited for | Teams seeking portable, open-source ML lifecycle tooling across environments | Teams seeking managed, integrated MLOps and AI delivery across Google Cloud |
FAQs
We can migrate experiments, run metadata, metrics, parameters, model artifacts, registered models, versions, tags, aliases, deployment logic, and connected workflows.
No. MLflow is a portable set of ML lifecycle tools, while Vertex AI is a broader managed AI platform. Each MLflow capability must be mapped separately.
Experiments and important runs are mapped to Vertex AI Experiments, including selected parameters, metrics, artifacts, and training information.
Selected run history can be transferred or archived. The practical scope depends on run volume, metadata quality, artifact locations, and retention requirements.
Important metrics and parameters are extracted and logged into Vertex AI Experiments or retained in an accessible historical archive.
Model files, plots, datasets, reports, and other artifacts can be moved from local storage, S3, Azure storage, or another location to Cloud Storage.
Assess your experiments, models, registries, artifacts, deployments, and MLOps workflows to build a practical Vertex AI migration roadmap.
Start Your Migration
