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**AD-TEAM** · Yesterday, 11:48 PM

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The Complete Mlops Product Design: AI Architecture Essential
Published 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 380.25 MB | Duration: 1h 49m

Build, Deploy & Scale Production-Ready MLOps Features

What you'll learn
MLOps roles are specific to Machine learning and operations that revolve around it.
MLOps involves changing requirements from customers in a fast and dynamic timeline.
MLOps has increased to different product development .
When Machine learning model changes in its existance then MLOps use cases starts like IoT or Others.

Requirements
Basic understanding of data structures and algorithms
Basic understanding of statistical concepts
Basic understanding of Machine learning Algorithms
Fundamentals of calculus and linear algebra and Math formulations

Description
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 scenariosML Model Registry - Building and managing centralized model repositories for version control and governanceML Model Metadata - Implementing robust tracking systems for model lineage, metrics, and deployment historyML Hyperparameter Optimization - Advanced techniques for automated model tuning and performance optimizationML Model Pipeline - Designing scalable, automated workflows for model training, validation, and deploymentML Model Profiling - Performance analysis and optimization techniques for production ML systemsML Model Packaging - Best practices for creating reproducible, deployable model artifactsML Model Resource Manager - Efficient resource allocation and management for ML workloadsML Model Retraining - Implementing automated retraining workflows and data drift detectionML Model in Cloud - Cloud-native architectures and deployment strategies for scalable AI systems

Machine learning engineer,AI Engineer,AI Architect,Machine Learning Architect,Software architect,AI Product developer,Product Architect

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