Databricks to Vertex AI Migration Services

Why Vertex AI?

Build and Operate AI on a Managed Platform

Managed Model Development

Use managed notebooks, training jobs, experiments, pipelines, and scalable compute for machine learning development.

Centralized Model Management

Register, version, govern, deploy, and manage models through Vertex AI Model Registry.

Production-Ready MLOps

Automate training and deployment workflows using Vertex AI Pipelines, metadata, evaluation, and monitoring capabilities.

Generative AI and Gemini

Access Gemini, Model Garden, model customization, evaluation, grounding, and agent-building capabilities through Vertex AI.

How We Migrate

A Structured Databricks to Vertex AI Migration

1

Assess

Review notebooks, training code, MLflow experiments, models, features, pipelines, endpoints, libraries, data dependencies, and governance.
2

Design

Define the Vertex AI architecture for development, training, experiments, pipelines, registry, deployment, monitoring, security, and data access.
3

Migrate Development Workloads

Adapt notebooks, code, environments, dependencies, datasets, and feature processes for Vertex AI Workbench and managed training.
4

Migrate Models and MLOps

Move model artifacts, experiment tracking, pipeline logic, registry processes, serving configurations, and monitoring workflows.
5

Validate and Deploy

Compare model metrics, predictions, latency, scalability, security, and business outcomes before transitioning production endpoints.

Why Codimite?

End-to-End Machine Learning Platform Modernization

Codimite combines Google Cloud architecture, data science, machine learning engineering, MLOps, and generative AI expertise.

Start Your Migration

Model and Dependency Assessment. We identify frameworks, runtimes, libraries, artifacts, training data, feature dependencies, and deployment requirements.
Vertex AI Architecture. We map Databricks ML capabilities to Vertex AI training, experiments, pipelines, registry, endpoints, monitoring, and generative AI services.
Reproducible MLOps Migration. We rebuild training and deployment workflows with versioned components, metadata, evaluation, automation, and governance.
Controlled Model Transition. We reduce production risk through shadow testing, parallel inference, model comparison, staged traffic, and rollback planning.

Platform Comparison

Databricks Machine Learning and Vertex AI at a Glance

Both platforms support machine learning operations, but Vertex AI provides a purpose-built Google Cloud environment for managed AI development and deployment.

Area	Databricks Machine Learning	Vertex AI
Platform approach	ML and AI capabilities integrated into the Databricks lakehouse	Managed AI platform for building, training, deploying, evaluating, and governing models
Development environment	Databricks notebooks using cluster or serverless compute	Vertex AI Workbench and managed notebook environments integrated with Google Cloud
Training	Training runs on Databricks clusters or serverless compute	Managed custom training, distributed training, hyperparameter tuning, and specialized compute
Experiment tracking	MLflow experiments and runs	Vertex AI Experiments with integrated metadata and pipeline tracking
Model registry	MLflow Model Registry integrated with Unity Catalog	Vertex AI Model Registry for centralized model versioning, governance, and deployment
Pipeline orchestration	Databricks workflows and MLflow-based processes	Vertex AI Pipelines for reusable and automated ML workflows
Feature management	Databricks feature engineering and online tables	Vertex AI Feature Store and BigQuery-centered feature-management options
Model deployment	Databricks Model Serving endpoints	Managed Vertex AI endpoints for online prediction and batch prediction
Monitoring	Databricks monitoring and platform-specific observability	Vertex AI Model Monitoring, evaluation, logging, and Cloud Monitoring integration
AutoML	Databricks AutoML capabilities	Vertex AI AutoML for supported tabular, image, text, and video use cases
Generative AI	Foundation models, Mosaic AI, agents, and model serving	Gemini, Model Garden, tuning, evaluation, grounding, RAG, and agent-development capabilities
Model choice	Databricks-hosted and external foundation models	Google models and selected open and third-party models through Model Garden
Data integration	Closely integrated with Delta Lake and Unity Catalog	Closely integrated with BigQuery, Cloud Storage, Dataflow, Dataproc, and Google Cloud databases
Governance	Unity Catalog governs data, features, models, and AI assets	IAM, Model Registry, metadata, audit logging, data governance, and organization policies
Compute	Uses Databricks clusters, GPUs, or serverless compute	Managed CPU, GPU, TPU, distributed training, and configurable prediction resources
MLOps automation	Databricks jobs, repos, MLflow, and deployment workflows	Vertex AI Pipelines, Cloud Build, Artifact Registry, Model Registry, and deployment automation
Operations	Teams manage Databricks workspaces, compute policies, and ML assets	Google manages core AI service infrastructure while teams manage models and applications
Best suited for	ML teams working within a Databricks lakehouse environment	Teams building managed ML and generative AI solutions across Google Cloud

FAQs

Databricks to Vertex AI Migration FAQs

What can be migrated from Databricks to Vertex AI?

Codimite can migrate notebooks, training code, MLflow experiments, model artifacts, feature workflows, pipelines, model-serving endpoints, monitoring, and connected AI applications.

Is Vertex AI a direct replacement for Databricks Machine Learning?

Not directly. Databricks capabilities must be mapped to the appropriate Vertex AI, BigQuery, Dataproc, or supporting Google Cloud service based on the workload.

Can Databricks notebooks, model code, and MLflow models be reused?

Often yes. However, filesystem paths, data access, Spark dependencies, libraries, model formats, distributed-training logic, and serving requirements may need to be adapted.

What happens to MLflow experiments and the Model Registry?

Experiment parameters, metrics, artifacts, and metadata can be mapped to Vertex AI Experiments. Model versions, aliases, approval processes, and deployment workflows can be redesigned using Vertex AI Model Registry.

How are Databricks Feature Store, workflows, and model endpoints migrated?

Feature logic may move to Vertex AI Feature Store, BigQuery, or another suitable architecture. Workflows can be rebuilt with Vertex AI Pipelines, while endpoints are redesigned using Vertex AI online or batch prediction.

How do you validate a Databricks to Vertex AI migration?

We compare feature values, evaluation metrics, predictions, model behavior, latency, throughput, monitoring, security, and business acceptance criteria before production cutover.