Latency monitoring per step in Agentic Ai - Model Pipeline Trace

Model Pipeline - Latency monitoring per step

This pipeline tracks how long each step takes during a machine learning model's training and prediction process. It helps find slow parts to improve speed.

Data Flow - 6 Stages

1Data Loading

1000 rows x 10 columns→Load raw data from source→1000 rows x 10 columns

Raw data with 10 features per sample

↓

2Preprocessing

1000 rows x 10 columns→Clean and normalize data→1000 rows x 10 columns

Normalized feature values between 0 and 1

↓

3Feature Engineering

1000 rows x 10 columns→Create new features→1000 rows x 15 columns

Added 5 new features derived from original data

↓

4Model Training

800 rows x 15 columns→Train model on training set→Trained model

Model learns patterns from 800 samples

↓

5Model Evaluation

200 rows x 15 columns→Evaluate model on test set→Accuracy and loss metrics

Accuracy: 85%, Loss: 0.35

↓

6Prediction

1 row x 15 columns→Make prediction on new data→Predicted class label

Predicted label: Class 2

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Initial training with high loss and low accuracy
2	0.50	0.72	Loss decreased, accuracy improved
3	0.40	0.80	Model learning well, metrics improving
4	0.35	0.85	Loss continues to decrease, accuracy high
5	0.33	0.87	Training converging with stable metrics

Prediction Trace - 5 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer 1 (ReLU)

Layer 3: Hidden Layer 2 (ReLU)

Layer 4: Output Layer (Softmax)

Layer 5: Prediction

Model Quiz - 3 Questions

Which pipeline stage increases the number of features?

A Feature Engineering

B Preprocessing

C Model Training

D Prediction

Key Insight

Monitoring latency at each step helps identify slow parts in the ML pipeline. This allows focused improvements to speed up training and prediction without losing accuracy.