ML Pipelines

- Overview

A machine learning (ML) pipeline is a series of steps that control the flow of data into and out of a ML model. It consists of the following steps: raw data input, features, outputs, ML model and model parameters, and prediction outputs.

A ML pipeline is an integrated, end-to-end workflow for developing ML models. Because ML is an integral part of many modern applications, organizations must have reliable and cost-effective processes for feeding operational data into ML models.

A ML pipeline is a way to code and automate the workflow required to generate ML models. A ML pipeline consists of sequential steps that perform everything from data extraction and preprocessing to model training and deployment. ML pipeline enables the sequence data to be transformed and correlated together in a model to analyzed and achieve outputs. 

ML pipeline is constructed to allow the flow of data from raw data format to some valuable information. It provides a mechanism to build a Multi-ML parallel pipeline system to examine different ML methods' outcomes. 

The objective of a ML pipeline is to exercise control over the ML model. A well-planned pipeline helps to makes the implementation more flexible. 

- Data Integration and Pipeline Tools

Data integration in a ML pipeline is the process of finding, moving, and combining data from different sources to create a unified view. This process can help ML and AI projects in a number of ways, including:

• Data enrichment: Combining data with external APIs, geospatial data, or social media data.
• Data analysis: Understanding data characteristics, patterns, and trends.
• Data preparation: Making data ready for ML models and algorithms through techniques like cleansing, transformation, normalization, encoding, scaling, imputation, and feature engineering
• Informed decision-making: Creating a coherent and accurate view of data from different formats and systems, such as databases, data warehouses, or API.

Data integration and pipeline tools can help teams discover, transform, and combine data for ML, analytics, data warehousing, and application development. Automated AI data pipelines can also streamline data processing, manage large volumes, ensure consistency, and reduce manual data preparation.