Coursera–MLOps|机器学习运营专业化

成为一名机器学习工程师。使用MLOps提升您的编程技能，这个全面的课程系列非常适合拥有编程知识的个人，如软件开发人员、数据科学家和研究人员。您将获得关键的MLOps技能，包括使用Python和Rust，利用GitHub Copilot提高生产率，以及利用Amazon SageMaker、Azure ML和MLflow等平台。您还将学习如何使用Hugging Face微调大型语言模型(LLM ),并了解ONNX格式的可持续和高效的二进制嵌入式模型的部署，为您在不断发展的MLOps领域取得成功做好准备，Coursera – MLOps | Machine Learning Operations Specialization

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
类型:电子教学|语言:英语+ srt英文字幕 |时长:25小时55分钟|大小:4.12 GB

你会学到什么
掌握Python基础知识、MLOps原则和数据管理，以便在生产环境中构建和部署ML模型。

利用Amazon Sagemaker / AWS、Azure、MLflow和Hugging Face实现端到端ML解决方案、管道创建和API开发。

使用带有拥抱脸的ONNX格式微调和部署大型语言模型(LLM)和容器化模型。

使用MLflow设计完整的MLOps管道，管理项目、模型和跟踪系统功能。

你将获得的技能
数据管理
Devops

MLOps

机器学习

开源代码库

Python编程

数据分析

微软Azure

大数据

亚马逊网络服务(亚马逊AWS)

云计算

Rust编程

通过这个系列，你将开始学习各种职业道路的技能

1.数据科学-分析和解释复杂的数据集，开发ML模型，实施数据管理，并推动数据驱动的决策制定。

2.机器学习工程-设计、构建和部署ML模型和系统，以解决现实世界的问题。

3.云ML解决方案架构师——利用AWS和Azure等云平台，以可扩展、经济高效的方式构建和管理ML解决方案。

4.人工智能(AI)产品管理——在商业、工程和数据科学团队之间架起桥梁，交付有影响力的AI/ML产品。

应用学习项目

通过动手练习和Github库探索和实践您的MLOps技能。

1.构建Python脚本以自动化机器学习模型的数据预处理和特征提取。

2.使用AI pair编程和GitHub Copilot开发真实世界的ML/AI解决方案，展示您与AI合作的能力。

4.使用Gradio、Hugging Face和Click框架为ML模型交互创建web应用程序和命令行工具。

3.使用Rust实现GPU加速的ML任务，以提高性能和效率。

4.在Amazon SageMaker和Azure ML上为基于云的MLOps培训、优化和部署ML模型。

5.使用MLflow设计完整的MLOps管道，管理项目、模型和跟踪系统功能。

6.微调和部署大型语言模型(LLM)和容器化模型，使用ONNX格式和拥抱脸。创建交互式演示，有效展示您的工作和进步。

Become a Machine Learning Engineer. Level-up your programming skills with MLOps

What you’ll learn
Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.

Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.

Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.

Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.

Skills you’ll gain
Data Management
Devops

MLOps

Machine Learning

Github

Python Programming

Data Analysis

Microsoft Azure

Big Data

Amazon Web Services (Amazon AWS)

Cloud Computing

Rust Programming

This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You’ll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You’ll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps

Through this series, you will begin to learn skills for various career paths

1. Data Science – Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.

2. Machine Learning Engineering – Design, build, and deploy ML models and systems to solve real-world problems.

3. Cloud ML Solutions Architect – Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.

4. Artificial Intelligence (AI) Product Management – Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.

Applied Learning Project

Explore and practice your MLOps skills with hands-on practice exercises and Github repositories.

1. Building a Python script to automate data preprocessing and feature extraction for machine learning models.

2. Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.

4. Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.

3. Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.

4. Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.

5. Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.

6. Fine-tuning and deploying Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face. Creating interactive demos to effectively showcase your work and advancements.