ML Phase 3

Model Development &
Training

The core of intelligence. Selecting the right algorithms, training models on GPU clusters, tuning hyperparameters, and rigorous evaluation.

Service Selection

Build vs. Buy Strategy

This phase demands selecting the right tool for the job. Should you use a Pre-trained API (like Neural TTS or ASR) for speed, or train a Custom Model on bespoke hardware for competitive advantage? We guide you through the trade-offs regarding quotas, cost, and capability.

Decision Engine

Pre-trained APIs (Buy)
Custom Models (Build)
Data Availability
Zero data needed. Ready out-of-the-box.
Requires massive labeled datasets (>10k samples).
Expertise Required
Developer friendly (REST APIs).
Requires Data Scientists & ML Engineers.
Customization
Limited fine-tuning options.
Full control over architecture & weights.
Time to Market
Days / Weeks.
Months.
Recommendation Logic: Start with Pre-trained APIs to validate business value (POC). Only transition to Custom Models if you have specific domain data that the generic models fail to capture, or if cost-at-scale justifies the engineering overhead.
Model Training

The Learning Process

Training is where the machine actually "learns". We rigorously Split Data to prevent leakage, optimize Loss Functions via Gradient Descent, allocate the right Compute Resources (GPU clusters), and define Retraining Strategies for lifecycle management.

Data Splitting Strategy

Training Set (70%)Validation (15%)Test (15%)
Learn
Tune
Verify
K-Fold Cross Validation
Fold 1
Fold 2
Fold 3
Fold 4
Fold 5

For smaller datasets, we use Cross Validation to rotate the validation set across all data partitions, ensuring the model isn't biased by a specific data slice.

Hyperparameter Tuning

Optimizing Model Architecture

Models are not "one size fits all". This phase is about finding the perfect configuration. We systematically tune Regularization (to prevent overfitting), Architecture (Layers/Nodes), and Optimization settings to maximize generalization performance.

Architecture Studio

Params: 52 (Est)
Learning Rate: 0.001
VISUALIZATION
Dropped Active
Layers (Depth)3
Nodes (Width)4
Dropout Rate0.2
Prevents overfitting by randomly disabling neurons.
f(x) = max(0, x). Standard for hidden layers. Avoids vanishing gradient.
Best for ReLU. Maintains variance across layers.
Performance Evaluation

Beyond Simple Accuracy

Evaluating ML models requires nuanced metrics. We analyze Bias vs. Variance, interpret Confusion Matrices based on business cost, and perform A/B Testing to validate real-world impact.

Metric Interpretation Studio

Fraud Detection Matrix
95
True Positive (TP)
Correctly identified target
100
False Positive (FP)
Type I Error (False Alarm)
5
False Negative (FN)
Type II Error (Missed)
9800
True Negative (TN)

"In fraud detection, missing a fraudulent transaction (False Negative) is costly. We prioritize Recall."

PrecisionTP / (TP + FP)
0.487
RecallTP / (TP + FN)
0.950
F1 Score (Harmonic Mean)0.644

Next Step: Operations