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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mlflow-course/
课程评论：没有评论
课程名称：MLflow in Action - 掌握使用 MLflow 工具的 MLOps 艺术 课程概述： 为什么选择 MLOps？MLOps 是现代机器学习工作流程的核心，解决了将机器学习模型在生产系统中投入使用的紧迫问题。传统上需要数月才能将机器学习模型投入生产的过程，现在借助 MLOps 工具可以在短短几天内完成。根据市场上的技术讨论，2024 年将是 MLOps 的时代，这将成为企业机器学习项目的必要技能。 为什么选择 MLflow 工具进行 MLOps？ MLflow 是 MLOps 的终极工具，因为它简化了整个机器学习生命周期。它允许你在一个统一的平台上高效跟踪实验、打包代码、注册版本和部署模型。与其他工具相比，MLflow 简化了过程，使得从开发到部署的过渡变得无缝。MLflow 的受欢迎程度不言而喻，数千家从初创企业到财富500强公司均已将 MLflow 集成到他们的 MLOps 工作流程中。 课程内容包括： - 理解 MLOps 的基础知识，以及传统机器学习生命周期的局限性，学习 MLOps 如何克服这些局限性。 - 从零开始全面讲解 MLflow 的概念到实时实施。 - 实际学习 MLflow 的四个核心组件：跟踪、模型、项目和注册。 - MLflow 中各种日志功能，用于精确跟踪和记录实验、运行、工件、参数、代码、指标等。 - 学习如何在 MLflow 中使用 Python 处理自定义模型。 - 掌握如何通过 MLflow 库、用户界面、MLflow 客户端和命令行接口与 MLflow 进行交互。 - 学习在实时 MLOps/MLflow 项目中遵循的最佳实践和优化技术。 **独家内容** - 一个完整的端到端机器学习项目，演示 MLflow 与 AWS 云的集成。在 AWS 云中使用 AWS Sagemaker、Codecommit、EC2、ECR、AWS S3、IAM 等服务构建、训练、测试和部署机器学习模型，同时利用 MLflow 跟踪功能。 完成本课程后，您可以充满信心地开始任何 MLOps/MLflow 项目。附加内容包括快速答疑及课程中使用的代码和参考资料，方便学习。
Why MLOps ?MLOps is the backbone of modern Machine learning workflows. It solves the pressing problem of operationalizing the ML models in production systems. Pushing the ML models to production which could traditionally take months can now be operationalized in few days using MLOps tools. As per the tech talks in market, 2024 is the year of MLOps and would become the mandate skill for Enterprise ML projects.Why MLflow tool for MLOps ?MLflow is the ultimate tool for MLOps because it streamlines the entire Machine learning lifecycle. It allows you to efficiently track experiments, package code, register versions and deploy models, all within one unified platform. Unlike other tools, MLflow simplifies the process, enabling you to transition from development to deployment seamlessly.MLflow's popularity is evident from the thousands of organizations, ranging from startups to Fortune 500 companies, that have integrated MLflow into their MLOps workflows._____________________________________________________________________________________________________What's included in this MLflow course ?Understand MLOps basics, limitations of traditional ML lifecycles, how MLOps overcomes those limitations.Complete MLflow concepts explained from Scratch to Real-Time implementation.Learn in practical the 4 core components of MLflow - Tracking, Model, Project, and Registry.Various logging functions in MLflow for precise tracking and recording of experiments, runs, artifacts, parameters, code, metrics, and more.Learn to handle customized models using Python in MLflow.Learn to interact with MLflow using MLflow library, UI, MLflow Client and CLI commands.Learn Best practices and Optimization techniques to follow in Real-Time MLOps/MLflow Projects.______________________________________________________________________________________________________**Exclusive** - A complete end-to-end ML project demonstrating MLflow's integration with AWS cloud. Build, Train, Test, Deploy a Machine learning model in AWS cloud using AWS Sagemaker, Codecommit, Ec2, ECR, AWS S3, IAM etc services while leveraging MLflow tracking capabilities.After completing this course, you can start working on any MLOps/MLflow project with full confidence.Add-Ons- Questions and Queries will be answered very quickly.- Codes and references used in lectures are attached in the course for your convenience.
