AI/ML Migration: Vertex AI and BigQuery ML to Azure AI

Comparative positioning note

This document is written from the perspective of Microsoft Azure, Cloud Scale Analytics, and CSA Loom. Any description of third-party or competing products, services, pricing, or capabilities is derived from publicly available documentation and sources believed accurate at the time of writing, and is provided for general comparison only. We do not claim expertise in, or authority over, any non-Microsoft product or service; the respective vendor's official documentation is the authoritative source for their offerings, which may change over time. Nothing here is intended to disparage any vendor — where a competing product has genuine advantages, we aim to note them honestly. Verify all third-party details against the vendor's current official documentation before making decisions.

A hands-on guide for data scientists and ML engineers migrating from Google Cloud AI/ML services to Azure Machine Learning, Databricks MLflow, Azure OpenAI, and Azure AI Search.

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Scope

This guide covers:

• Vertex AI Training to Azure ML / Databricks ML
• AutoML to Azure AutoML / Databricks AutoML
• Vertex AI Pipelines to Azure ML Pipelines / Prompt Flow
• Vertex AI Endpoints to Azure ML Managed Endpoints
• BigQuery ML to Fabric ML / Databricks MLflow
• Gemini to Azure OpenAI (GPT-4o, o3, o4-mini)
• Vertex AI Search to Azure AI Search
• Vertex AI Agents to Azure AI Agents / Copilot Studio

For compute migration (BigQuery SQL, Dataproc), see Compute Migration.

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Architecture overview

flowchart LR
    subgraph GCP["GCP AI/ML"]
        VT[Vertex AI Training]
        VA[Vertex AI AutoML]
        VP[Vertex AI Pipelines]
        VE[Vertex AI Endpoints]
        BQML[BigQuery ML]
        GEM[Gemini]
        VS[Vertex AI Search]
        VAG[Vertex AI Agents]
    end

    subgraph Azure["Azure AI/ML"]
        AML[Azure ML]
        AAML[Azure AutoML]
        AMLP[Azure ML Pipelines]
        AME[Azure ML Managed Endpoints]
        MLF[Databricks MLflow]
        AOAI[Azure OpenAI]
        AIS[Azure AI Search]
        AIA[Azure AI Agents / Copilot Studio]
    end

    VT --> AML
    VT --> MLF
    VA --> AAML
    VP --> AMLP
    VE --> AME
    BQML --> MLF
    GEM --> AOAI
    VS --> AIS
    VAG --> AIA

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Vertex AI Training to Azure ML / Databricks ML

Vertex AI Training provides managed compute for custom model training using TensorFlow, PyTorch, scikit-learn, and XGBoost. The Azure equivalents are Azure ML and Databricks ML.

Mapping

Vertex AI concept	Azure ML equivalent	Databricks equivalent	Notes
Custom training job	Azure ML command job	Databricks notebook job	Submit training code to managed compute
Training pipeline	Azure ML pipeline	Databricks Workflow	Multi-step training orchestration
Managed dataset	Azure ML data asset	Unity Catalog table / volume	Versioned data for training
Experiment tracking	Azure ML experiments	MLflow experiments	Metrics, parameters, artifacts
Model registry	Azure ML model registry	MLflow Model Registry	Versioned model management
Hyperparameter tuning	Azure ML sweep jobs	Optuna / Hyperopt on Databricks	Automated hyperparameter search
Distributed training	Azure ML distributed training	Databricks distributed Spark ML	Multi-node training
Custom containers	Azure ML environments (Docker)	Databricks cluster libraries	Runtime dependency management
TensorBoard	Azure ML TensorBoard integration	MLflow + TensorBoard on Databricks	Training visualization

Migration approach

1. Training code -- Python training scripts using TensorFlow/PyTorch/scikit-learn transfer with minimal changes. Remove Vertex AI SDK imports (google.cloud.aiplatform) and replace with Azure ML SDK (azure.ai.ml) or MLflow.
2. Data access -- Replace gs:// paths with abfss:// paths for ADLS or Delta table references.
3. Experiment tracking -- Replace Vertex AI experiment logging with MLflow log_metric(), log_param(), log_artifact().
4. Model registry -- Register trained models in MLflow Model Registry or Azure ML model registry.

Example: Training script migration

Vertex AI:

from google.cloud import aiplatform

aiplatform.init(project="acme-gov", location="us-central1")

job = aiplatform.CustomTrainingJob(
    display_name="sales-forecast",
    script_path="train.py",
    container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-12:latest",
    requirements=["pandas", "scikit-learn"]
)

model = job.run(
    dataset=dataset,
    model_display_name="sales-forecast-v1",
    args=["--epochs=50", "--batch-size=32"]
)

Azure ML:

from azure.ai.ml import MLClient, command, Input
from azure.identity import DefaultAzureCredential

ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group, workspace)

job = command(
    code="./src",
    command="python train.py --epochs 50 --batch-size 32",
    environment="AzureML-sklearn-1.0@latest",
    compute="gpu-cluster",
    inputs={"data": Input(type="uri_folder", path="azureml://datastores/training/paths/sales/")}
)

returned_job = ml_client.jobs.create_or_update(job)

Databricks MLflow:

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestRegressor

mlflow.set_experiment("/sales-forecast")

with mlflow.start_run():
    model = RandomForestRegressor(n_estimators=100)
    model.fit(X_train, y_train)

    mlflow.log_param("n_estimators", 100)
    mlflow.log_metric("rmse", rmse)
    mlflow.sklearn.log_model(model, "model", registered_model_name="sales-forecast")

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AutoML migration

Vertex AI AutoML to Azure AutoML

Vertex AI AutoML feature	Azure AutoML equivalent	Notes
Tabular classification	AutoML classification	Direct equivalent
Tabular regression	AutoML regression	Direct equivalent
Tabular forecasting	AutoML forecasting	Direct equivalent
Image classification	AutoML image classification	Direct equivalent
Object detection	AutoML object detection	Direct equivalent
Text classification	AutoML NLP	Direct equivalent
Video classification	Custom Azure ML	Less automated; use custom pipeline

Vertex AI AutoML to Databricks AutoML

Databricks AutoML provides automated ML for tabular data with a notebook-based UI.

Feature	Vertex AI AutoML	Databricks AutoML	Azure AutoML
Tabular data	Yes	Yes	Yes
Image/video	Yes	No	Yes
Text/NLP	Yes	No	Yes
Explainability	Feature importance	SHAP values	Feature importance + SHAP
Model export	TF SavedModel	MLflow model	ONNX / MLflow
Code generation	No	Yes (generates notebook)	No
Custom preprocessing	Limited	Full notebook control	Featurization config

Recommendation: Use Databricks AutoML for tabular data (it generates editable notebooks). Use Azure AutoML for image, video, and NLP tasks.

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Vertex AI Pipelines to Azure ML Pipelines / Prompt Flow

Vertex AI Pipelines uses Kubeflow Pipelines (KFP) DSL. Azure provides two pipeline systems:

Azure ML Pipelines

For traditional ML workflows (data prep, training, evaluation, deployment).

KFP concept	Azure ML Pipeline equivalent	Notes
@component decorator	Azure ML component	Reusable pipeline step
@pipeline decorator	Azure ML pipeline	Pipeline definition
Input / Output	Input / Output	Data flow between steps
Artifact	Azure ML data asset	Pipeline artifacts
Container component	Azure ML environment	Runtime specification
Compiler	ml_client.jobs.create_or_update()	Pipeline submission

Prompt Flow (Azure AI Foundry)

For LLM-based workflows (RAG, agents, evaluation).

Vertex AI feature	Prompt Flow equivalent	Notes
AIP Logic	Prompt Flow DAG	LLM orchestration
Chatbot Studio	Copilot Studio	No-code agent builder
Vertex AI evaluation	Prompt Flow evaluation	LLM evaluation framework
Grounding	Azure AI Search retrieval	RAG pipeline

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Vertex AI Endpoints to Azure ML Managed Endpoints

Vertex AI Endpoints feature	Azure ML Managed Endpoints	Notes
Online prediction	Managed online endpoint	Real-time inference
Batch prediction	Managed batch endpoint	Batch inference
Traffic splitting	Traffic allocation (A/B)	Blue-green deployment
Auto-scaling	Instance auto-scaling	Scale based on load
Model monitoring	Azure ML model monitoring	Data drift, prediction drift
Private endpoint	Private managed endpoint	VNet integration

Databricks alternative: Databricks Model Serving provides a simpler deployment path for models tracked in MLflow.

# Azure ML managed endpoint deployment
from azure.ai.ml.entities import ManagedOnlineEndpoint, ManagedOnlineDeployment

endpoint = ManagedOnlineEndpoint(name="sales-forecast-endpoint", auth_mode="key")
ml_client.online_endpoints.begin_create_or_update(endpoint)

deployment = ManagedOnlineDeployment(
    name="blue",
    endpoint_name="sales-forecast-endpoint",
    model="azureml:sales-forecast:1",
    instance_type="Standard_DS3_v2",
    instance_count=1
)
ml_client.online_deployments.begin_create_or_update(deployment)

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BigQuery ML to Databricks MLflow

BigQuery ML's CREATE MODEL syntax is uniquely simple. The migration to MLflow requires a shift from inline SQL to a notebook-based workflow, but gains the full MLflow lifecycle (experiment tracking, model registry, serving, monitoring).

Model type mapping

BigQuery ML model	MLflow / Databricks equivalent	Notes
LINEAR_REG	scikit-learn LinearRegression + MLflow	Standard regression
LOGISTIC_REG	scikit-learn LogisticRegression + MLflow	Classification
KMEANS	scikit-learn KMeans + MLflow	Clustering
BOOSTED_TREE_REGRESSOR	XGBoost / LightGBM + MLflow	Gradient boosting
BOOSTED_TREE_CLASSIFIER	XGBoost / LightGBM + MLflow	Gradient boosting
RANDOM_FOREST_REGRESSOR	scikit-learn RandomForest + MLflow	Ensemble
DNN_REGRESSOR	PyTorch / TensorFlow + MLflow	Deep learning
ARIMA_PLUS	Prophet / statsmodels + MLflow	Time series
MATRIX_FACTORIZATION	Surprise / implicit + MLflow	Recommendation
TRANSFORM (feature eng)	Spark feature engineering / dbt	Preprocessing

Migration example

BigQuery ML:

CREATE OR REPLACE MODEL `acme-gov.ml.sales_forecast`
OPTIONS(
  model_type='BOOSTED_TREE_REGRESSOR',
  input_label_cols=['revenue'],
  data_split_method='AUTO_SPLIT'
) AS
SELECT region, product_category, month, revenue
FROM `acme-gov.finance.training_data`;

-- Prediction
SELECT * FROM ML.PREDICT(MODEL `acme-gov.ml.sales_forecast`,
  (SELECT region, product_category, month FROM `acme-gov.finance.scoring_data`));

Databricks MLflow:

import mlflow
import mlflow.xgboost
import xgboost as xgb
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

# Load training data from Delta
train_df = spark.table("finance.training_data").toPandas()
X = train_df[["region", "product_category", "month"]]
y = train_df["revenue"]

mlflow.set_experiment("/sales-forecast")

with mlflow.start_run():
    model = xgb.XGBRegressor(n_estimators=100, max_depth=6)
    model.fit(X, y)

    mlflow.log_params({"n_estimators": 100, "max_depth": 6})
    mlflow.xgboost.log_model(model, "model", registered_model_name="sales-forecast")

# Prediction using ai_query (Databricks SQL)
# SELECT ai_query('sales-forecast', region, product_category, month) FROM finance.scoring_data;

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Gemini to Azure OpenAI

Gemini model	Azure OpenAI equivalent	Notes
Gemini 2.0 Flash	GPT-4o-mini	Fast, cost-efficient
Gemini 2.0 Pro	GPT-4o	Strong general purpose
Gemini 1.5 Pro (long context)	GPT-4.1 (long context)	Extended context window
Gemini Ultra	o3 / o4-mini	Advanced reasoning

API migration

Vertex AI (Gemini):

from vertexai.generative_models import GenerativeModel

model = GenerativeModel("gemini-2.0-pro")
response = model.generate_content("Summarize the quarterly report")
print(response.text)

Azure OpenAI:

from openai import AzureOpenAI

client = AzureOpenAI(
    azure_endpoint="https://acme-gov.openai.azure.com/",
    api_key=os.environ["AZURE_OPENAI_API_KEY"],
    api_version="2024-06-01"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarize the quarterly report"}]
)
print(response.choices[0].message.content)

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Vertex AI Search to Azure AI Search

Vertex AI Search feature	Azure AI Search equivalent	Notes
Unstructured search	Full-text search	BM25 ranking
Structured search	Faceted search + filters	Rich filtering
Hybrid search (semantic + keyword)	Hybrid search (semantic + BM25)	Direct equivalent
Vector search	Vector search (HNSW)	Embedding-based retrieval
Grounding / RAG	RAG with AI Search retriever	Enterprise RAG pattern
Data connectors	Indexers (Blob, SQL, Cosmos DB)	Automated indexing
Snippets / extractive answers	Semantic answers	AI-enhanced results
Conversation search	Conversational search	Multi-turn queries

RAG pipeline migration

Vertex AI Search + Gemini RAG becomes Azure AI Search + Azure OpenAI RAG:

# Azure RAG pattern
from azure.search.documents import SearchClient
from openai import AzureOpenAI

# 1. Retrieve relevant documents
search_client = SearchClient(endpoint, index_name, credential)
results = search_client.search(query, top=5, query_type="semantic")

# 2. Build context from search results
context = "\n".join([r["content"] for r in results])

# 3. Generate answer with context
openai_client = AzureOpenAI(...)
response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": f"Answer based on this context:\n{context}"},
        {"role": "user", "content": query}
    ]
)

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Vertex AI Agents to Azure AI Agents / Copilot Studio

Vertex AI Agents feature	Azure equivalent	Notes
Agent Builder (no-code)	Copilot Studio	No-code agent builder
Agent Builder (code)	Azure AI Agents (Semantic Kernel)	Code-first agent framework
Tool use / function calling	Function calling (Azure OpenAI)	Tool integration
Grounding (data store)	Azure AI Search grounding	RAG-based grounding
Multi-turn conversation	Copilot Studio / custom agent	Stateful conversation
Evaluation	Prompt Flow evaluation	Agent evaluation framework
Deployment	Azure AI Foundry deployment	Managed agent hosting

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Migration sequence

1. Inventory all Vertex AI models, endpoints, pipelines, and BigQuery ML models
2. Classify by type: traditional ML, AutoML, LLM, search, agents
3. Migrate traditional ML -- convert training scripts, set up MLflow tracking
4. Migrate AutoML -- retrain using Azure AutoML or Databricks AutoML
5. Migrate BigQuery ML -- convert CREATE MODEL to MLflow-based training
6. Migrate LLM workloads -- switch API calls from Gemini to Azure OpenAI
7. Migrate search/RAG -- rebuild search indexes in Azure AI Search
8. Migrate agents -- rebuild in Copilot Studio or with Semantic Kernel
9. Validate model performance parity (metrics comparison)

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Validation checklist

After migrating AI/ML:

• All ML models retrained and registered in MLflow or Azure ML
• Model performance metrics match or exceed GCP baselines
• Online endpoints serving predictions with acceptable latency
• Batch prediction pipelines producing matching output
• LLM integrations using Azure OpenAI with equivalent quality
• RAG pipelines returning relevant results from Azure AI Search
• Agents responding appropriately in Copilot Studio or custom framework
• Experiment tracking and model versioning operational in MLflow

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Last updated: 2026-04-30 Maintainers: CSA-in-a-Box core team Related: Compute Migration | Complete Feature Mapping | Migration Playbook