ML-Ops Platform v2.4.1-stable
End-to-end machine learning operations. Manage model lifecycle, automate CI/CD, track experiments, and monitor production drift in a unified pipeline architecture.
Pipeline Architecture
📥 Raw Data Ingestion
→
🧩 Feature Store
→
⚙️ Training Cluster
→
📊 Model Registry
→
🚀 Canary Deploy
→
📡 Real-time Monitoring
Live System Metrics
Active Pipelines
14
↑ 2 from last hour
Models in Registry
89
● 3 pending validation
Avg. Inference Latency
24ms
↓ 12% optimized
Data Drift Alerts
0
● Stable
.divisions/pipeline/ml_training.yaml
# ML-Ops Pipeline Definition pipeline: "customer-churn-v3" trigger: "schedule: 0 2 * * *" # Daily at 2 AM UTC stages: data_prep: runtime: "python:3.11-cpu" resources: "2vCPU, 8GB RAM" input: "s3://raw-events/partition=${today}" training: runtime: "python:3.11-gpu" resources: "T4-GPU, 16GB VRAM" hyperparams: "learning_rate": 0.001 "batch_size": 256 "epochs": 50 validation: metric_threshold: "auc > 0.85" rollback_policy: "auto" deployment: strategy: "canary: 20% → 50% → 100%" endpoint: "/api/v2/predict/churn"
churn-predictor-v3.2
● Production
Framework: PyTorch 2.1 • Size: 142MB
AUC: 0.912 | Precision: 0.88 | Recall: 0.85
churn-predictor-v3.1
● Staging
Framework: PyTorch 2.1 • Size: 138MB
AUC: 0.894 | Precision: 0.86 | Recall: 0.84
sentiment-analyzer-v1.0
● Archived
Framework: TensorFlow 2.12 • Size: 210MB
F1: 0.92 | Latency: 18ms
Experiment Run #4821
2025-01-14 08:32 UTC
Optimizer: AdamW
LR Schedule: CosineAnnealing
Dataset: Q4-2024-split
GPU Util: 94%
✓ Validation AUC: 0.912 (threshold: 0.85)
✓ Drift score: 0.02 (threshold: < 0.15)
➜ Promoted to Registry → Staging
kubectl get pods -n ml-serving
$ kubectl get pods -n ml-serving -o wide NAME READY STATUS AGE IP NODE GPU churn-predictor-v3-6b4f8d9c-xk2m 2/2 Running 14h 10.24.1.8 node-gpu-01 T4/1 churn-predictor-v3-6b4f8d9c-p9l4 2/2 Running 14h 10.24.1.11 node-gpu-02 T4/1 churn-predictor-v3-canary-z8n1 2/2 Running 2h 10.24.1.15 node-gpu-03 T4/0.5 $ kubectl scale deployment churn-predictor-v3 --replicas=4 deployment.apps/churn-predictor-v3 scaled
Deployment Console
❯ divisions deploy --pipeline ml-training.yaml --target staging
[12:42:01] Authenticating with AWS IAM Role...
[12:42:02] Packaging model artifact (churn-predictor-v3.2.tar.gz)...
[12:42:05] Pushing to ECR registry...
[12:42:08] ✓ Image pushed successfully
[12:42:09] Updating Kubernetes deployment manifest...
[12:42:11] Rolling update initiated (strategy: canary 20%)...
[12:42:15] ⚡ Canary traffic routed. Monitoring metrics for 300s...
[12:42:48] ✓ Latency: 22ms | Error Rate: 0.01% | Drift: 0.02
[12:42:52] ✓ Canary promotion to 100% complete.
❯
Supported Integrations
Python SDK
Native API for training & registry
AWS SageMaker
Managed endpoints & AutoML
Docker / OCI
Containerized model serving
MLflow / Weights
Experiment tracking sync
Prometheus/Grafana
Metrics & visualization
OpenPolicyAgent
RBAC & compliance gates