ML Phase 2
Data Engineering &
Preparation
Transforming raw data into model-ready features. Cleaning, scaling, normalization, and advanced feature engineering pipelines.
Transformation Architecture
Processing at Scale
This phase defines how we process data. For ML workloads, simple scripts aren't enough. We implement robust Data Transformation Solutions that can handle the volume, velocity, and variety of modern datasets using Distributed Computing.
Compute Strategy Selector
Why Distributed (MapReduce)?Necessary for ML-scale data (Petabytes). Splits tasks across a cluster of nodes.
Transformation Pattern
Transform in Transit (ETL)
Processing data *before* it lands in storage. Best for PII redaction and format standardization.
Transform in Place (ELT)
Loading raw data first, then transforming via SQL. Leverages warehouse compute power.
Distributed Processing Simulator
Framework: Apache Spark / Hadoop
1
Input
2
Split
3
Map
4
Shuffle
5
Reduce
6
Output
STATUS: RUNNINGSTEP: Input
Reading 10TB raw logs from S3...
Nodes
50+
Parallelism
High
Fault Tolerance
Auto-Retry
Sanitize & Prepare
Data Hygiene First
Garbage in, garbage out. Before modeling, we must Sanitize the dataset. This involves identifying missing values, handling corrupt records, and removing "stop words" from text. We then Format & Scale numerical features to ensure model convergence.
Data Cleaning Workbench
| Feature | Value | Status | Action Applied |
|---|---|---|---|
| Age | 25 | clean | - |
| Income | null | missing | - |
| City | Hong Kong | clean | - |
| Score | 9999 | outlier | - |
| Notes | The user is very happy. | text | - |
Missing Data
Strategy: Imputation (Mean)
Outliers
Strategy: Cap / Floor
Text Data
Strategy: Stop-word Removal
Feature Engineering
Signal from Noise
Raw data is rarely ready for modeling. Feature Engineering is the art of creating predictive signals. We extract features from unstructured data (Text, Images) and transform structured data using techniques like One-Hot Encoding, Binning, and Dimensionality Reduction.
Unstructured Data Extraction
NLP / Text
TF-IDF, Word2Vec, BERT Embeddings
Computer Vision
CNN Feature Maps, Pixel Normalization
Audio / Speech
Spectrograms, MFCCs, Fourier Transform
INPUT
"The quick brown fox..."
Tokenization
VECTOR
[ 1092, 331, 9921, 441 ... ]