Udemy – The Complete Mlops Product Design: Ai Architecture Essential

File Name:	Udemy – The Complete Mlops Product Design: Ai Architecture Essential
Content Source:	https://www.udemy.com/course/mlops-product-design-ai-architecture
Genre / Category:	Other Tutorials
File Size :	380 MB
Publisher:	udemy
Updated and Published:	February 09, 2025

Transform your ML models into production-ready AI features with our comprehensive MLOps product design course. Whether you’re a data scientist stepping into MLOps or a machine learning engineer looking to master end-to-end AI system design, this course equips you with battle-tested strategies for building, deploying, and maintaining ML models at scale.

Learn how to architect robust ML pipelines that stand up to real-world challenges. Through hands-on projects, you’ll master essential MLOps practices from model registry management to automated retraining workflows. Discover how to optimize your models for production, implement efficient resource management, and leverage cloud infrastructure for scalable AI solutions.

Perfect for: ML engineers, data scientists, AI architects, and technical professionals looking to master MLOps practices for production AI systems.

What You’ll Learn:

• MLOps Use Cases – Real-world applications and implementation strategies for different business scenarios
• ML Model Registry – Building and managing centralized model repositories for version control and governance
• ML Model Metadata – Implementing robust tracking systems for model lineage, metrics, and deployment history
• ML Hyperparameter Optimization – Advanced techniques for automated model tuning and performance optimization
• ML Model Pipeline – Designing scalable, automated workflows for model training, validation, and deployment
• ML Model Profiling – Performance analysis and optimization techniques for production ML systems
• ML Model Packaging – Best practices for creating reproducible, deployable model artifacts
• ML Model Resource Manager – Efficient resource allocation and management for ML workloads
• ML Model Retraining – Implementing automated retraining workflows and data drift detection
• ML Model in Cloud – Cloud-native architectures and deployment strategies for scalable AI systems

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