|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/mlops-product-design-ai-architecture/
课程评论：没有评论
课程名称：全面的 MLOps 产品设计：AI 架构必备知识 课程概述： 本课程旨在帮助学员将机器学习模型转化为生产就绪的 AI 功能。无论您是转向 MLOps 的数据科学家，还是希望掌握端到端 AI 系统设计的机器学习工程师，本课程提供经过实战检验的策略，以建设、部署和维护大规模的 ML 模型。课程内容涵盖如何架构稳健的 ML 管道以应对现实世界的挑战。通过实践项目，您将掌握从模型注册管理到自动化再训练工作流的基本 MLOps 实践。课程还将介绍如何优化模型以适应生产环境、实施高效的资源管理以及利用云基础设施实现可扩展的 AI 解决方案。 适合人群： 机器学习工程师、数据科学家、AI 架构师及希望掌握 MLOps 实践的技术专业人士。 学习内容： - MLOps 用例：不同商业场景下的实际应用及实施策略 - ML 模型注册：构建和管理集中式模型库，以实现版本控制和治理 - ML 模型元数据：实施模型血统、指标和部署历史的跟踪系统 - ML 超参数优化：自动化模型调优和性能优化的高级技术 - ML 模型管道：设计可扩展的模型训练、验证和部署的自动化工作流 - ML 模型分析：生产 ML 系统的性能分析和优化技术 - ML 模型打包：创建可重现和可部署模型工件的最佳实践 - ML 资源管理：ML 工作负载的高效资源配置和管理 - ML 模型再训练：实施自动化再训练工作流和数据漂移检测 - 云中的 ML 模型：可扩展 AI 系统的云原生架构和部署策略 通过本课程，学员将能够掌握构建高效、可持续的生产 AI 系统所需的关键 MLOps 实践。
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
