Here’s the Pipeline:
Last Updated on July 24, 2023 by Editorial Team
Author(s): Shaurya Lalwani
Originally published on Towards AI.
Photo by Andy Kelly on Unsplash
This article explains the important parts of a Regression/Classification Pipeline (the differences have been shown wherever required). Additional points can be added based on the domain and industry you’re working for. Generally, model deployment and cloud integration follow this process, but that’s not what we’re talking about today.
Another point which has not been highlighted as such below is the “data cleaning” to be done before it is wrangled and mined, as this is probably the most important part of diving into analytics: Cleaning the data, and transforming it, such that it makes sense, and such… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.