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Everything you need to know about MLOps (Machine Learning Operations)! I'll explain what MLOps is, why it's essential, and how it solves real-world problems when deploying ML models to production.

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▬▬▬▬▬▬  What You'll Learn 🧠  ▬▬▬▬▬▬
- What MLOps is and why it's necessary for ML systems
- Real-world challenges when deploying ML models (using a banking fraud detection example)
- How MLOps differs from traditional software development
- Key MLOps concepts: containerization, CI/CD pipelines, monitoring, and data drift
- Essential MLOps tools: Docker, Kubernetes, MLflow, TensorFlow Data Validation, Prometheus, Grafana, and more
- Complete MLOps workflow from data collection to production deployment
- Who needs to learn MLOps: Data Scientists, DevOps Engineers, Cloud Engineers, and Engineering Managers


▬▬▬▬▬▬ T I M E S T A M P S ⏰  ▬▬▬▬▬▬
00:00 - Intro
00:39 - Understanding the problem - Why we need MLOps
04:30 - How MLOps solves this
10:08 - Why MLOps Became Necessary
11:42 - MLOps Tools and Implementation
18:01 - What Does an MLOps Workflow Look Like
20:52 - Who Needs To Learn MLOps


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MLOps Explained | What is MLOps | MLOps Tutorial | What is Machine Learning Operations | What is AI Operations