| Workflow Orchestration | Purpose-built ML pipeline orchestration with pluggable backends — Airflow, Kubeflow, Kubernetes, Vertex AI, and more | Vertex AI Pipelines is a managed, production-grade orchestrator for containerized ML workflows on GCP with console visibility and lifecycle tracking |
| Integration Flexibility | Composable stack with 50+ MLOps integrations — swap orchestrators, trackers, and deployers without code changes | Deep integration within GCP via Google Cloud Pipeline Components, but no cloud-agnostic integration model for non-GCP tools |
| Vendor Lock-In | Open-source Python pipelines run anywhere — switch clouds, orchestrators, or tools without rewriting code | Runs inside a GCP project/region with GCP identity and GCS storage — migration typically means re-platforming the entire pipeline stack |
| Setup Complexity | pip install zenml — start building pipelines in minutes with zero infrastructure, scale when ready | Managed service eliminates infrastructure setup — configure GCP project, IAM, and storage to get production-grade pipelines running |
| Learning Curve | Python-native API with decorators — familiar to any ML engineer or data scientist who writes Python | Requires learning KFP component/pipeline DSL, compilation workflows, containerization patterns, and GCP resource concepts |
| Scalability | Delegates compute to scalable backends — Kubernetes, Spark, cloud ML services — for unlimited horizontal scaling | Enterprise-scale workloads on GCP — orchestrates large training/processing jobs using Google-managed Vertex, BigQuery, and Dataflow services |
| Cost Model | Open-source core is free — pay only for your own infrastructure, with optional managed cloud for enterprise features | Documented per-run pipeline fee ($0.03/run) plus underlying compute costs — Google provides cost labeling and billing export for transparency |
| Collaboration | Code-native collaboration through Git, CI/CD, and code review — ZenML Pro adds RBAC, workspaces, and team dashboards | Collaborative use through shared GCP projects, IAM-based access control, and console-based visibility into runs and metadata |
| ML Frameworks | Use any Python ML framework — TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM — with native materializers and tracking | Broad framework support via custom containers and prebuilt container images for common frameworks including PyTorch and TensorFlow |
| Monitoring | Integrates Evidently, WhyLogs, and other monitoring tools as stack components for automated drift detection and alerting | Vertex AI Model Monitoring provides scheduled monitoring jobs with alerting when model quality metrics cross defined thresholds |
| Governance | ZenML Pro provides RBAC, SSO, workspaces, and audit trails — self-hosted option keeps all data in your own infrastructure | Enterprise governance via GCP IAM, network controls, billing attribution, and VPC support for pipeline-launched resources |
| Experiment Tracking | Native metadata tracking plus seamless integration with MLflow, Weights & Biases, Neptune, and Comet for rich experiment comparison | Vertex AI Experiments tracks hyperparameters, environments, and results with SDK and console support built on Vertex ML Metadata |
| Reproducibility | Automatic artifact versioning, code-to-Git linking, and containerized execution guarantee reproducible pipeline runs | Pipeline templates plus Vertex ML Metadata record artifacts and lineage graphs — strong primitives for reproducing ML workflows on GCP |
| Auto-Retraining | Schedule pipelines via any orchestrator or use ZenML Pro event triggers for drift-based automated retraining workflows | Vertex AI scheduler API supports one-time or recurring pipeline runs for continuous training patterns within GCP |
from zenml import pipeline, step, Model
from zenml.integrations.mlflow.steps import (
mlflow_model_deployer_step,
)
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import numpy as np
@step
def ingest_data() -> pd.DataFrame:
return pd.read_csv("data/dataset.csv")
@step
def train_model(df: pd.DataFrame) -> RandomForestRegressor:
X, y = df.drop("target", axis=1), df["target"]
model = RandomForestRegressor(n_estimators=100)
model.fit(X, y)
return model
@step
def evaluate(model: RandomForestRegressor, df: pd.DataFrame) -> float:
X, y = df.drop("target", axis=1), df["target"]
preds = model.predict(X)
return float(np.sqrt(mean_squared_error(y, preds)))
@step
def check_drift(df: pd.DataFrame) -> bool:
# Plug in Evidently, Great Expectations, etc.
return detect_drift(df)
@pipeline(model=Model(name="my_model"))
def ml_pipeline():
df = ingest_data()
model = train_model(df)
rmse = evaluate(model, df)
drift = check_drift(df)
# Runs on any orchestrator, logs to MLflow,
# tracks artifacts, and triggers retraining — all
# in one portable, version-controlled pipeline
ml_pipeline()from kfp import dsl, compiler
from google.cloud import aiplatform
PROJECT_ID = "my-gcp-project"
REGION = "europe-west1"
PIPELINE_ROOT = "gs://my-bucket/pipeline-root"
@dsl.component
def preprocess(input_uri: str) -> str:
# Read and clean data from GCS
return input_uri
@dsl.component
def train(data_uri: str) -> str:
# Train model and write artifacts to GCS
return f"{data_uri}#trained-model"
@dsl.pipeline(name="train-pipeline", pipeline_root=PIPELINE_ROOT)
def pipeline(input_uri: str = "gs://my-bucket/data/train.csv"):
data = preprocess(input_uri=input_uri)
train(data_uri=data.output)
# Compile pipeline to JSON template
compiler.Compiler().compile(
pipeline_func=pipeline, package_path="pipeline.json"
)
# Submit to Vertex AI (GCP-only)
aiplatform.init(project=PROJECT_ID, location=REGION)
job = aiplatform.PipelineJob(
display_name="train-pipeline",
template_path="pipeline.json",
pipeline_root=PIPELINE_ROOT,
)
job.submit()
# Pipeline runs only on GCP — no built-in
# portability to AWS, Azure, or on-prem.
# Metadata tied to Vertex ML Metadata service.
.png)
