Deploying ML Models in Production: An Overview
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YouTubeValerio Velardo - The Sound of AI
Deploying ML Models in Production: An Overview
The deployment of ML models in production is a delicate process filled with challenges. You can deploy a model via a REST API, on an edge device, or as as an off-line unit used for batch processing. You can build the deployment pipeline from scratch, or use ML deployment frameworks. In this video, you'll learn about the different strategies to ...
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These projects will start building your confidence ⬇️ 1. End-to-End Machine Learning Pipeline : - Building a Complete Machine Learning Pipeline in Python : https://youtu.be/HZ9MUzCRlzI?si=q_KGiS2yrN3u7gbF - Deploying Machine Learning Models with Flask and Docker : https://youtu.be/UbCWoMf80PY?si=WuQyqwV-dH-CNDHZ 2. Personalized Recommendation System: - Building a Movie Recommendation System: https://www.youtube.com/watch?v=9gBC9R-msAk - Recommendation Systems Explained: https://www.youtube.com/w
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These projects will start building your confidence ⬇️ 1. End-to-End Machine
Michael Lin
🚀 What does a Machine Learning (ML) Engineer actually do? From cleaning messy data to deploying intelligent models in production, ML Engineers handle it all! Their role involves: 🔹 Data Preparation & Cleaning 🔹 Model Development 🔹 Feature Engineering 🔹 Model Evaluation & Tuning 🔹 Deployment & Monitoring They bridge the gap between data science & real-world applications, ensuring AI solutions run smoothly at scale. 🌍✨ 📩 For details & training: support@uplatz.com Follow - @uplatz_global #m
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🚀 What does a Machine Learning (ML) Engineer actually do? From cleaning
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