Online Learning & Drift Detection
প্রতি ৩ ঘণ্টায় স্বয়ংক্রিয় model retrain · উন্নত হলেই deploy
Retraining Strategy
Frequency
৩ ঘণ্টা
Auto scheduled
Drift Threshold
১০%
CPU বা RAM mean drift
Deploy Policy
উন্নত হলে
Only if RMSE improves
মোট Retrain
50
এখন পর্যন্ত
🚀 সফল Deploy
25
মোট 50 retrain এর মধ্যে
📉 সর্বশেষ CPU RMSE
5.68%
আগে ছিল: 5.68%
⚡ সর্বশেষ CPU Drift
31.5%
Threshold: 10% · V1001
সর্বশেষ Retrain এর প্রভাব — Real-time Database থেকে
🧠 CPU Forecast — V1001 · 2026-08-13 09:47
আগে
5.68%
RMSE
→
পরে
5.68%
RMSE
0% ↑
খারাপ
Drift: 31.5%
· R²: 0.480 → 0.479
· Samples: 28,752
💾 RAM Forecast — V1001 · 2026-08-13 09:47
আগে
3.02%
RMSE
→
পরে
2.99%
RMSE
1% ↓
উন্নত
RAM Drift: 5.7%
· Deploy: ✅ হয়েছে
RMSE উন্নতির গ্রাফ
CPU RMSE (%)
RAM RMSE (%)
Retrain ইতিহাস (সর্বশেষ 50টি)
| সময় | VPS | CPU আগে | CPU পরে | RAM আগে | RAM পরে | CPU Drift | Samples | Status |
|---|---|---|---|---|---|---|---|---|
| 2026-08-13 09:47 | V1001 | 5.6780% | 5.6788% ↑ | 3.0205% | 2.9948% ↓ | 31.5% | 28,752 | ✅ Deploy |
| 2026-08-13 06:46 | V1001 | 5.6679% | 5.6676% ↓ | 3.0417% | 3.0660% ↑ | 13.8% | 28,716 | ✅ Deploy |
| 2026-08-13 03:46 | V1001 | 5.6750% | 5.6719% ↓ | 3.0480% | 3.0394% ↓ | 28.7% | 28,680 | ✅ Deploy |
| 2026-08-13 00:45 | V1001 | 5.6810% | 5.6857% ↑ | 3.0815% | 3.0551% ↓ | 19.1% | 28,644 | ✅ Deploy |
| 2026-08-12 21:45 | V1001 | 5.6734% | 5.6767% ↑ | 3.0902% | 3.0760% ↓ | 25.3% | 28,607 | ✅ Deploy |
| 2026-08-12 18:44 | V1001 | 5.6866% | 5.6792% ↓ | 3.1044% | 3.1116% ↑ | 31.5% | 28,571 | ✅ Deploy |
| 2026-08-12 15:44 | V1001 | 5.7017% | 5.7060% ↑ | 3.2793% | 3.1114% ↓ | 7.3% | 28,535 | ✅ Deploy |
| 2026-08-12 12:43 | V1001 | 5.7004% | 5.6956% ↓ | 3.3944% | 3.4004% ↑ | 15.8% | 28,499 | ✅ Deploy |
| 2026-08-12 09:43 | V1001 | 5.6851% | 5.6903% ↑ | 3.4222% | 3.4126% ↓ | 5.7% | 28,463 | ✅ Deploy |
| 2026-08-12 06:42 | V1001 | 5.6829% | 5.6793% ↓ | 3.4324% | 3.4172% ↓ | 18.1% | 28,427 | ✅ Deploy |
| 2026-08-12 03:41 | V1001 | 5.6921% | 5.6871% ↓ | 3.4493% | 3.4468% ↓ | 28.0% | 28,391 | ✅ Deploy |
| 2026-08-12 00:41 | V1001 | 5.7059% | 5.7036% ↓ | 3.4541% | 3.4559% ↑ | 13.4% | 28,355 | ✅ Deploy |
| 2026-08-11 21:40 | V1001 | 5.7011% | 5.7005% ↓ | 3.4669% | 3.4975% ↑ | 21.0% | 28,319 | ✅ Deploy |
| 2026-08-11 18:40 | V1001 | 5.7074% | 5.7037% ↓ | 3.4716% | 3.5073% ↑ | 16.1% | 28,282 | ✅ Deploy |
| 2026-08-11 15:39 | V1001 | 5.6948% | 5.7026% ↑ | 3.4713% | 3.4811% ↑ | 27.0% | 28,246 | ⏭️ Skip |
| 2026-08-11 12:39 | V1001 | 5.6841% | 5.6898% ↑ | 3.4687% | 3.4927% ↑ | 34.9% | 28,210 | ⏭️ Skip |
| 2026-08-11 09:39 | V1001 | 5.6842% | 5.6879% ↑ | 3.4665% | 3.4882% ↑ | 39.3% | 28,174 | ⏭️ Skip |
| 2026-08-11 06:39 | V1001 | 5.6875% | 5.7020% ↑ | 3.4689% | 3.4881% ↑ | 44.4% | 28,138 | ⏭️ Skip |
| 2026-08-11 03:38 | V1001 | 5.6908% | 5.7023% ↑ | 3.4621% | 3.4729% ↑ | 35.8% | 28,102 | ⏭️ Skip |
| 2026-08-11 00:38 | V1001 | 5.6864% | 5.6790% ↓ | 3.4677% | 3.4638% ↓ | 33.7% | 28,066 | ✅ Deploy |
| 2026-08-10 21:37 | V1001 | 5.6777% | 5.6769% ↓ | 3.4711% | 3.4862% ↑ | 35.0% | 28,030 | ✅ Deploy |
| 2026-08-10 18:36 | V1001 | 5.6883% | 5.6897% ↑ | 3.4638% | 3.4700% ↑ | 35.7% | 27,994 | ⏭️ Skip |
| 2026-08-10 15:36 | V1001 | 5.6901% | 5.6948% ↑ | 3.4569% | 3.4954% ↑ | 39.0% | 27,958 | ⏭️ Skip |
| 2026-08-10 12:36 | V1001 | 5.6839% | 5.6845% ↑ | 3.4513% | 3.4743% ↑ | 32.3% | 27,922 | ⏭️ Skip |
| 2026-08-10 09:35 | V1001 | 5.6957% | 5.6974% ↑ | 3.4572% | 3.4946% ↑ | 29.6% | 27,886 | ⏭️ Skip |
| 2026-08-10 06:35 | V1001 | 5.6923% | 5.6947% ↑ | 3.4586% | 3.4997% ↑ | 26.6% | 27,849 | ⏭️ Skip |
| 2026-08-10 03:35 | V1001 | 5.6979% | 5.7079% ↑ | 3.4599% | 3.4772% ↑ | 33.9% | 27,813 | ⏭️ Skip |
| 2026-08-10 00:34 | V1001 | 5.6916% | 5.7029% ↑ | 3.4605% | 3.4725% ↑ | 33.1% | 27,777 | ⏭️ Skip |
| 2026-08-09 21:34 | V1001 | 5.6995% | 5.6938% ↓ | 3.4505% | 3.4865% ↑ | 26.9% | 27,741 | ✅ Deploy |
| 2026-08-09 18:33 | V1001 | 5.6823% | 5.6872% ↑ | 3.4507% | 3.4662% ↑ | 13.6% | 27,705 | ⏭️ Skip |
| 2026-08-09 15:33 | V1001 | 5.6709% | 5.6771% ↑ | 3.4400% | 3.4623% ↑ | 31.3% | 27,669 | ⏭️ Skip |
| 2026-08-09 12:32 | V1001 | 5.6607% | 5.6614% ↑ | 3.4389% | 3.4677% ↑ | 36.5% | 27,633 | ⏭️ Skip |
| 2026-08-09 09:32 | V1001 | 5.6655% | 5.6651% ↓ | 3.4277% | 3.4611% ↑ | 35.8% | 27,597 | ✅ Deploy |
| 2026-08-09 06:31 | V1001 | 5.6516% | 5.6631% ↑ | 3.4300% | 3.4628% ↑ | 38.1% | 27,561 | ⏭️ Skip |
| 2026-08-09 03:31 | V1001 | 5.6653% | 5.6842% ↑ | 3.4582% | 3.4843% ↑ | 28.6% | 27,525 | ⏭️ Skip |
| 2026-08-09 00:31 | V1001 | 5.6591% | 5.6679% ↑ | 3.4681% | 3.4921% ↑ | 21.3% | 27,489 | ⏭️ Skip |
| 2026-08-08 21:30 | V1001 | 5.6788% | 5.6915% ↑ | 3.4717% | 3.4844% ↑ | 21.9% | 27,453 | ⏭️ Skip |
| 2026-08-08 18:30 | V1001 | 5.6783% | 5.7023% ↑ | 3.4796% | 3.4801% ↑ | 27.1% | 27,417 | ⏭️ Skip |
| 2026-08-08 15:30 | V1001 | 5.6894% | 5.7031% ↑ | 3.4949% | 3.5281% ↑ | 28.0% | 27,380 | ⏭️ Skip |
| 2026-08-08 12:29 | V1001 | 5.7086% | 5.7046% ↓ | 3.4799% | 3.4852% ↑ | 41.8% | 27,344 | ✅ Deploy |
| 2026-08-08 09:29 | V1001 | 5.7162% | 5.7224% ↑ | 3.4817% | 3.4878% ↑ | 31.7% | 27,308 | ⏭️ Skip |
| 2026-08-08 06:29 | V1001 | 5.7247% | 5.7255% ↑ | 3.4893% | 3.4815% ↓ | 28.8% | 27,272 | ✅ Deploy |
| 2026-08-08 03:28 | V1001 | 5.7279% | 5.7284% ↑ | 3.4850% | 3.4878% ↑ | 26.9% | 27,236 | ⏭️ Skip |
| 2026-08-08 00:28 | V1001 | 5.7152% | 5.7152% ↑ | 3.4646% | 3.4646% ↑ | 9.9% | 27,200 | ⏭️ Skip |
| 2026-08-07 21:27 | V1001 | 5.7256% | 5.7152% ↓ | 3.4646% | 3.4755% ↑ | 43.0% | 27,164 | ✅ Deploy |
| 2026-08-07 18:27 | V1001 | 5.7401% | 5.7372% ↓ | 3.4675% | 3.4613% ↓ | 34.0% | 27,128 | ✅ Deploy |
| 2026-08-07 15:26 | V1001 | 5.7580% | 5.7539% ↓ | 3.5643% | 3.4662% ↓ | 32.9% | 27,092 | ✅ Deploy |
| 2026-08-07 12:26 | V1001 | 5.7626% | 5.7621% ↓ | 3.5839% | 3.6213% ↑ | 32.6% | 27,056 | ✅ Deploy |
| 2026-08-07 09:25 | V1001 | 5.7596% | 5.7577% ↓ | 3.5840% | 3.6043% ↑ | 30.8% | 27,020 | ✅ Deploy |
| 2026-08-07 06:25 | V1001 | 5.7719% | 5.7729% ↑ | 3.5861% | 3.5951% ↑ | 37.5% | 26,983 | ⏭️ Skip |
কীভাবে কাজ করে
📊
ডেটা সংগ্রহ
প্রতি ৩০ সেকেন্ডে নতুন metrics
📈
Drift শনাক্ত
নতুন vs পুরনো mean (threshold 10%)
🔄
Retrain
XGBoost নতুন ডেটা দিয়ে train
✅
যাচাই
পুরনো vs নতুন RMSE তুলনা
🚀
Deploy
উন্নত হলেই replace