ML Phase 1

Exploration &
Discovery

Defining the business problem, identifying data sources, establishing ingestion pipelines, and performing initial Exploratory Data Analysis (EDA).

Problem Formulation

Translating Business to Math

The most common cause of AI failure isn't code—it's solving the wrong problem. We map your Business Goals to the precise Machine Learning Task required to achieve them.

Decision Matrix: When to use ML?

Problem Complexity

Use Machine Learning

Patterns are too complex or dynamic for manual coding (e.g., Vision, NLP).

Use Rule Engine

Logic can be written in < 100 'If-Then' statements.

Data Volume

Use Machine Learning

Massive datasets requiring automated pattern recognition.

Use Rule Engine

Small datasets where manual inspection is feasible.

Adaptability

Use Machine Learning

Environment changes frequently; system needs to learn from new data.

Use Rule Engine

Environment is static; rules rarely change.

Learning Paradigms

Labeled Data
Input + Target Answer
Input
Email Text
Label
SPAM

The model learns to map inputs to known outputs. Used for Classification and Regression.

Task Taxonomy

Business Question

"Is this A or B?"

Use Case Example
Fraud Detection (Fraud vs. Legit)
Technical Definition

Predicting a category or class label.

Supervised Learning
Data Repositories

Identify & Architect

This phase is the foundation of any ML initiative. We conduct a comprehensive Data Source Audit to identify where your value lies (Content & Location). Then, we map these sources to the correct Storage Mediums—balancing cost, latency, and throughput.
Recommended Storage Medium

Object Storage (Data Lake)

The foundation of modern ML. We architect scalable Object Stores to hold petabytes of raw data (images, documents) immutably.

Hierarchy: Flat namespace with metadata tags.
Lifecycle: Auto-tiering to cold storage for cost efficiency.
Access: HTTP/REST API based.
Infrastructure Decision
Best for: Raw ingestion layer and archival.
Ingestion & Orchestration

Pipeline Architecture

This phase focuses on identifying the correct Data Job Style (Batch vs. Streaming) and implementing a robust Orchestration Layer to manage dependencies, retries, and scheduling.

Batch ETL Pipeline

Periodic Schedules (Cron)Bulk Data TransferHigh Latency Tolerance
Relational DB
Source
Bulk Extract Job
Ingest
Data Lake (Raw)
Storage

Workflow Orchestrator

Dependency Management & Scheduling

Running
Ingest_Raw
Validate_Schema
Transform_Features
Load_FeatureStore
Trigger: Schedule (@daily)Retry Policy: Exponential BackoffConcurrency: 50 Nodes
Analysis & Visualization

Unlocking Patterns with EDA

Before modeling, we must understand the data's story. We use Exploratory Data Analysis (EDA) to uncover hidden correlations, detect seasonality, and validate hypotheses using statistical tests.

Data Visualization

Identifies correlation and outliers between variables.

Next Step: Preparation