Full model lifecycle management with production-grade MLOps
Manage the complete ML lifecycle — from experiment tracking and model registry to canary deployments, drift monitoring, and automated retraining — all in one platform.
The Challenges
Experiment Sprawl
Tracking hundreds of experiments across notebooks, scripts, and team members without a central registry leads to duplicated work.
Deployment Risk
Rolling out new models in production without proper canary testing or rollback capabilities risks serving bad predictions.
Model Staleness
Production models silently degrade as data distributions shift, and manual retraining cycles cannot keep pace.
How CorePlexML Helps
Experiment Tracking & Registry
Every experiment is logged with hyperparameters, metrics, and artifact lineage. The model registry tracks promotion history.
Learn moreDeployment Strategies
Direct, canary, blue-green, and shadow deployments with traffic splitting, A/B testing, and automatic rollback.
Learn moreAuto-Retraining Pipelines
Configure drift-triggered, schedule-based, or performance-based retraining with automatic promotion when validation passes.
Learn moreSDK Example
from coreplexml import CorePlexMLClient
client = CorePlexMLClient(
base_url="https://api.coreplexml.io",
api_key="sk_your_api_key"
)
# Train and compare models
experiment = client.experiments.create(
project_id="proj_fraud",
dataset_version_id="dsv_txns_v3",
target_column="is_fraud",
max_models=50,
max_runtime_secs=1200
)
# Canary deploy the leader
deployment = client.deployments.create(
project_id="proj_fraud",
model_id=experiment["leader_model_id"],
strategy="canary",
traffic_percentage=5
)
# Set up auto-retraining on drift
client.retraining.create_policy(
deployment_id=deployment["id"],
trigger="drift",
threshold=0.05
)Expected Impact
Ready to get started?
Try CorePlexML free — no credit card required. Train your first model in under 10 minutes.