ML Engineer: Deploy & Scale AI Models in Production скачать в хорошем качестве

ML Engineer: Deploy & Scale AI Models in Production

🚀 Become a Machine Learning Engineer! Learn how to deploy and scale AI models in real-world production systems. 🤖 This complete guide covers the ML production pipeline from data collection to live deployment. Discover key challenges like performance, scalability, and monitoring that every ML Engineer must master. 🐳 Master essential technologies: Docker containerization, Kubernetes orchestration, AWS SageMaker, Google Vertex AI, and Azure ML Services. 💻 See real code examples for model serving with Flask APIs and learn continuous monitoring techniques to keep models accurate over time. 🔧 Build skills in Python programming, cloud infrastructure, and data engineering to land high-paying ML Engineer roles. #MachineLearning #MLOps #AI #DataScience #DevOps #Kubernetes #Docker #CloudComputing #ProductionML Chapters: 00:00 - Machine Learning Engineer: Building AI Production Systems 00:28 - What Does an ML Engineer Do? 01:05 - ML Production Pipeline 01:42 - Key Challenges in Production 02:23 - Deployment Technologies 03:01 - Model Serving Example 03:40 - Monitoring and Maintenance 04:25 - Key Skills Required 05:13 - Outro 🔗 Stay Connected: ▶️ YouTube:    / @thecodelucky   📱 Instagram:   / thecodelucky   📘 Facebook:   / codeluckyfb   🌐 Website: https://codelucky.com ⭐ Support us by Liking, Subscribing, and Sharing! 💬 Drop your questions in the comments below 🔔 Hit the notification bell to never miss an update #CodeLucky