API-first ML integration with CI/CD-ready SDKs
Integrate ML capabilities into your applications with a clean REST API and Python SDK. Automate model training, deployment, and predictions in your existing CI/CD pipelines.
The Challenges
ML Integration Complexity
Connecting ML models to application code requires juggling serialization formats, serving infrastructure, and version management.
Environment Inconsistency
Models trained in notebooks behave differently in production due to dependency mismatches and data pipeline differences.
Deployment Automation
Manual model deployment breaks CI/CD workflows and slows feature releases that depend on ML predictions.
How CorePlexML Helps
REST API & Python SDK
320+ API endpoints and a typed Python SDK let you train, deploy, and predict with familiar HTTP calls and code patterns.
Learn moreProgrammatic MLOps
Script deployments, retraining policies, and monitoring alerts — all through the SDK with CI/CD integration points.
Learn moreDataset Builder API
Automate data cleaning and feature engineering pipelines through the conversational Dataset Builder API.
Learn moreSDK Example
from coreplexml import CorePlexMLClient
client = CorePlexMLClient(
base_url="https://api.coreplexml.io",
api_key="sk_your_api_key"
)
# Full CI/CD workflow in one script
project = client.projects.create(name="churn-v2")
dataset = client.datasets.upload(
project_id=project["id"],
file_path="data/customers.csv"
)
experiment = client.experiments.create(
project_id=project["id"],
dataset_version_id=dataset["dataset_version_id"],
target_column="churned",
max_runtime_secs=300
)
deployment = client.deployments.create(
project_id=project["id"],
model_id=experiment["leader_model_id"],
strategy="canary",
traffic_percentage=10
)
# Predict in your app
result = client.deployments.predict(
deployment_id=deployment["id"],
features={"tenure": 24, "monthly_charges": 79.99}
)Expected Impact
Ready to get started?
Try CorePlexML free — no credit card required. Train your first model in under 10 minutes.