AI and Cloud Solutions

Use Case No.03

Data Preprocessing Automation for AI/ML Pipelines

Use Case:

Event: When raw data is ingested from various sources (e.g., databases, APIs, IoT sensors).

Problem: AI/ML workflows often require large-scale data preprocessing before feeding it into machine learning models. Automating the extraction, transformation, and loading (ETL) of data reduces time and effort in AI/ML data preparation.

Workflow:

Trigger ETL Workflow

An event triggers the ETL workflow for data preprocessing.

Data Extraction and Cleaning

The platform’s modular workflow images extract data, clean it (removing null values or errors), normalize it, and format it as required by the AI/ML pipeline.

Feature Engineering

Workflow images can also apply feature engineering, such as encoding categorical variables or scaling numerical data, before passing it to the ML model.

Push to AI/ML Pipeline

Once preprocessing is completed, the data is automatically pushed to the AI/ML pipeline for training or inference.

Outcome: Automated data preprocessing reduces manual effort and speeds up the preparation of clean, usable data for AI/ML pipelines, improving the efficiency of data science teams.

Coredge, is a solutions-focused company using AI, cloud and other digital technologies to address complex industry challenges and empower clients to thrive in the digital era.

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