Data Extraction, Validation & Preparation for ML Training | MLOps EP 3

Welcome to Episode 3 of the MLOps series! In this video, we dive deep into the essential first steps of any Machine Learning (ML) workflow — Data Extraction, Validation, and Preparation.

🎯 What You’ll Learn in This Video:

📥 Data Extraction: How to source and extract data from various platforms, databases, and APIs efficiently.
✅ Data Validation: Techniques to ensure data quality, detect anomalies, and handle missing values.
🛠️ Data Preparation: Transforming raw data into a usable format for ML models, including feature engineering, normalization, and encoding.
⚙️ Why Watch This?
Building a robust ML model starts with clean, well-prepared data. This episode covers practical strategies and tools that ensure your data pipeline is optimized for successful training and deployment.

🚀 Who Is This For?

MLOps enthusiasts
Data Scientists & ML Engineers
DevOps professionals transitioning into MLOps
Anyone looking to streamline their ML data workflows
🔗 Related Videos
1️⃣ Introduction to MLOps | EP 1:    • MLOps - Introduction  | Learn MLOps In Sim...  
2️⃣ Setting Up ML Pipelines | EP 2:    • AI & ML Explained for MLOps Engineers | ML...  

💬 Let’s Connect!
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