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AI Generated Summary

Churn Prediction v3.3 is 68% through training with validation accuracy trending toward 95.4%, ahead of the previous version. GPU utilization is at 72% across the training cluster with headroom for 2 more concurrent jobs.

247 models across the fleet · 18.4M predictions/day served View model fleet

Active Training Jobs

Churn Prediction v3.3

68%

Epoch 34/50 · dataset: churn_q3_v2 · ETA 22 min

Demand Forecast v2.4

41%

Epoch 8/20 · dataset: demand_seasonal · ETA 48 min

Sentiment Analysis v1.5

12%

Epoch 3/25 · dataset: support_tickets_2026 · ETA 2h 10m

3 jobs training · 1 queued behind current cluster load View all models

GPU Utilization

Cluster-wide resource allocation

GPU 1

72%

GPU 2

54%

GPU 3

38%

Headroom for 2 more concurrent jobs

Hyperparameter Configuration

Learning Rate

0.001

Batch Size

256

Optimizer

Adam

Epochs

50

Weight Decay

0.0001

Early Stopping

Patience 5

Applied to Churn Prediction v3.3

Dataset Selection

4 datasets available · 18.4M rows in churn_q3_v2 Manage datasets

Validation Accuracy

Per-epoch validation vs. training accuracy

Best epoch: 34 · 95.4% validation accuracy +2.1pts vs. v3.2

Loss Curves

Training vs. validation loss

Validation loss down to 0.18 · no overfitting detected Epoch 34/50

Training Queue

Scheduled jobs · estimated duration

4 jobs queued · next slot opens in 22 min

Failed Training Jobs

Product Recommender v1.0 · OOM error
Inventory Optimizer v0.9 · data schema mismatch
Fraud Detection v4.2 · gradient explosion
3 failures this week · 1.1% of all runs View error logs

AI Optimization Suggestions

Reduce batch size to 128 for Sentiment Analysis v1.5 to avoid memory pressure on shared GPU nodes.
Schedule Demand Forecast v2.4 to start after 11 PM to run on a dedicated GPU with no contention.
2 suggestions · generated from cluster telemetry

Training History

Fraud Detection v4.1 completed · 98.9% accuracy3 hours ago
Customer Segmentation v1.9 completed · 91.3% accuracy5 min ago
Forecast Accuracy Model v5.2 completed · 97.8% accuracy1 day ago
API Availability Predictor v2.0 completed · 99.98% uptime match2 days ago
247 models trained to date across the fleet View full history