MLOps & AI Model Deployment Explained | From Training to Production | AI MLOps | Course 12

Description

@LearnAIWithBrema Building an AI model is only half the job — deploying, monitoring, and maintaining it in production is where real AI engineering begins. In Course 12: MLOps & AI Model Deployment, we explain how AI systems move from experiments to reliable, scalable, production-grade solutions, using clear concepts, real-world workflows, and practical examples. This course is part of the Learn AI with Brema advanced AI learning path. 🔍 What you’ll learn in this course: • What MLOps really is and why it matters • The AI model lifecycle: data → training → deployment → monitoring • Versioning for data, models, and experiments • CI/CD for machine learning • Model deployment strategies (batch, real-time, edge) • Monitoring model performance and drift • Retraining and lifecycle management • Security, governance, and compliance basics • Scaling AI systems in production 👥 Who this course is for: • AI learners moving toward real-world systems • ML & AI engineers • Data scientists • Platform and DevOps engineers • Leaders deploying AI at scale Some basic AI or ML knowledge is helpful but not required. 📌 If this video helps you: • Subscribe to Learn AI with Brema for advanced AI courses • Share this video with your friends and colleagues • Like the video to support the channel 🎥 Subscribe here: 👉 http://www.youtube.com/@LearnAIWithBrema 🌐 Free AI courses & structured learning: 👉 https://learn.human-reset-pause.com Learn AI clearly — from fundamentals to production systems — with Learn AI with Brema.