EPISODE · Jan 9, 2024 · 19 MIN
Key Concepts for Preparing Data in ML Pipelines
from 52 Weeks of Cloud · host Pragmatic AI Labs
Hey readers 👋, if you enjoyed this content, I wanted to share some of my favorite resources to continue your learning journey in technology!Hands-On Courses for Rust, Data, Cloud, AI and LLMs 🚀Rust Programming Specialization: https://insight.paiml.com/qwhRust for DevOps: https://insight.paiml.com/x14Rust LLMOps: https://insight.paiml.com/g3bRust Fundamentals: https://insight.paiml.com/qytData Engineering with Rust: https://insight.paiml.com/zm1Python and Rust with Linux Command Line Tools: https://insight.paiml.com/jot 🔥 Hot Course Offers:🤖 Master GenAI Engineering - Build Production AI Systems🦀 Learn Professional Rust - Industry-Grade Development📊 AWS AI & Analytics - Scale Your ML in Cloud⚡ Production GenAI on AWS - Deploy at Enterprise Scale🛠️ Rust DevOps Mastery - Automate Everything🚀 Level Up Your Career:💼 Production ML Program - Complete MLOps & Cloud Mastery🎯 Start Learning Now - Fast-Track Your ML Career🏢 Trusted by Fortune 500 TeamsLearn end-to-end ML engineering from industry veterans at PAIML.COM
Embed this episode
What this episode covers
This podcast covers core concepts around data wrangling including ETL vs ELT data pipelines, the iterative process of data discovery, structuring, cleaning, enriching, validating and publishing data. It compares traditional ETL flows for structured data vs ELT flows better suited for large volumes of raw, unstructured data destined for data lakes.
NOW PLAYING
Key Concepts for Preparing Data in ML Pipelines
No transcript for this episode yet
Similar Episodes
No similar episodes found.
Similar Podcasts
No similar podcasts found.