1. Overview of artificial intelligence and cloud computing systems
Artificial Intelligence (AI for short) is a science and engineering that simulates human intelligence by using computer systems to simulate, expand and enhance human intelligence capabilities. Artificial intelligence involves many fields, including machine learning, deep learning, natural language processing, computer vision, etc.
Cloud Computing is an Internet-based computing model that allocates computing tasks and resources to computer networks distributed in different locations to achieve resource sharing and on-demand use. In cloud computing, resources such as computing and data storage are no longer limited to local devices, but are provided to users through the cloud service provider's servers.
There is a close correlation between artificial intelligence and cloud computing systems. Cloud computing provides powerful computing power and storage resources for artificial intelligence, making large-scale data processing and complex algorithm training possible. Through the cloud computing platform, artificial intelligence applications can be developed, deployed, and debugged in an elastic computing environment, and computing resources can be flexibly expanded and contracted.
At the same time, artificial intelligence has also brought innovation and improvements to cloud computing systems. Through artificial intelligence technology, cloud computing services can provide more intelligent and personalized services, such as intelligent recommendation systems, automated operation and maintenance, and security monitoring. The development of artificial intelligence has also promoted the advancement of cloud computing systems, allowing cloud computing services to better adapt to changing business needs and complex data processing tasks.
To sum up, artificial intelligence and cloud computing systems complement and promote each other, jointly promoting the development of technology and business. They have a wide range of applications in various fields and will continue to influence and change the way we live and work.
2. Introduction to the Artificial Intelligence and Cloud Computing Training Room
2.1 Introduction to the construction of training room
The construction of the artificial intelligence and cloud computing training room aims to provide students with a place to practice and learn artificial intelligence and cloud computing technologies, and to cultivate their skills and abilities in this field. The following is a brief introduction to the construction of artificial intelligence and cloud computing training rooms:
Hardware facilities:
1. Computing resources: The training room needs to be equipped with high-performance computing equipment, including GPU servers and cloud computing clusters for model training and inference.
2. Student workstation: Each student is equipped with a computer workstation for code writing, data processing and experimental operations.
3. Cloud platform access: The training room needs to establish a connection with the cloud service provider so that students can use the cloud platform for experiments and project development.
software tools:
1. Development environment: Provide students with integrated development environments (IDEs) related to AI development and cloud computing, such as Jupyter Notebook, PyCharm, etc., as well as necessary programming languages and tools, such as Python, TensorFlow, PyTorch, etc.
2. Database and storage: Configure appropriate database and storage systems to store experimental data, model parameters and related resources.
3. Virtualization technology: Create virtual machines and container environments through virtualization technology to facilitate students to conduct experiments and tests.
education resources:
1. Experimental cases: Provide a wealth of experimental cases and sample codes, covering various directions and application scenarios of artificial intelligence and cloud computing, to help students understand and apply related technologies.
2. Teaching courseware: Prepare special teaching courseware to cover the basic knowledge, principles and algorithms of artificial intelligence and cloud computing to help students systematically learn and understand related concepts.
3. Practical training projects: Design real project training, simulate actual business scenarios and problems, and cultivate students' comprehensive abilities and problem-solving abilities.
Network and Security:
1. Fast and stable network connection: Provide high-speed and stable network connection to ensure that students can smoothly access the cloud platform and resources during the training process.
2. Security protection: Strengthen the security protection measures of the training room network, including firewalls, intrusion detection systems, etc., to protect the security of the training environment and data.
Administration and Support:
1. Management system: Establish a management system for the training room, including student account management, resource scheduling and monitoring functions to facilitate teachers to manage and guide the training process.
2. Technical support: Provide a professional technical support team to solve technical problems and difficulties encountered by students during the training process.
The construction of artificial intelligence and cloud computing training rooms requires comprehensive consideration of hardware facilities, software tools, teaching resources, network security and other aspects to meet the needs of students for learning and practice. Such training rooms can provide students with a good learning environment and cultivate their comprehensive abilities and innovative thinking in the fields of artificial intelligence and cloud computing.
2.2 Construction purpose
1. Provide practical opportunities: The training room provides students with a real practical environment, allowing them to develop, debug and experiment with artificial intelligence and cloud computing systems. Through practical operations, students can gain an in-depth understanding of the specific applications of related technologies and tools and master practical problem-solving abilities.
2. Develop skills: The practical training room aims to cultivate students’ practical skills and abilities in the fields of artificial intelligence and cloud computing. By participating in experiments and projects, students can become familiar with key technologies such as data processing, algorithm implementation, model training and deployment, and improve their programming and system operation capabilities.
3. Provide resource support: The training room is equipped with high-performance computing equipment and cloud service access to provide students with sufficient resource support. Students can use these resources for computationally intensive tasks, large-scale data processing, and complex model training, improving their ability to deal with big data and complex problems.
4. Cultivate teamwork skills: The construction of the training room encourages students to work in teams and complete projects and experiments together. This helps develop students' communication, collaboration and leadership skills and adapts them to team working environments and models.
5. Enhance innovation awareness: The training room stimulates students' creativity and innovation awareness by providing innovative projects and experimental cases. Students can explore new solutions and application scenarios in practice, try to improve existing technologies and develop new fields.
6. Master the latest technology: The construction of practical training rooms allows students to access and learn the latest artificial intelligence and cloud computing technologies. The technology in these fields is changing with each passing day. Through the practical training room, students can understand and apply the latest algorithms, frameworks and tools, and stay in line with the forefront of technology.
Through the realization of the above purposes, the Artificial Intelligence and Cloud Computing System Training Room provides students with a comprehensive and in-depth learning platform to help them become competitive professionals in this field. At the same time, the construction of the training room will also help promote the development of academic research and technological innovation, and promote progress in the fields of artificial intelligence and cloud computing.
3. Composition of artificial intelligence and cloud computing system training room
3.1 Artificial Intelligence and Cloud Computing Training Platform
The platform adopts a B/S structure and uses spring cloud microservice technology to build multiple stable and efficient service modules, provide SSO single sign-on service, and use unified identity authentication. Based on k8s, the platform implements multiple deployment methods of public cloud, hybrid cloud, and private cloud. It adopts MySQL cluster and MongoDB cluster. It can provide KVM and containers according to teaching needs to meet the virtualization requirements of cloud computing teaching. It can also allocate CPU, Memory resources provide highly reliable, dynamically scalable, and extensive teaching services for teaching practice. The main modules include course creation tools, assignments, activities, cloud disks, shared courses, my courses, and cloud preferred courses.
Multi-architecture cloud hosts: Cloud hosts with X86 and ARM architectures can be provided. Cloud hosts with corresponding architectures can be configured for different users according to user needs to meet different user needs.
Multiple virtualization technologies: The bottom layer integrates two virtualization technologies, docker and openstack, giving users more choices. Different virtualization technologies can be selected according to different technical needs.
Automatic scheduling of platform resources: Through background resource monitoring, the platform automatically suspends the virtualized resources of users who are inactive within a specified period and restores them when they are in use, achieving elastic automatic scheduling of virtualized resources and using less hardware resources to meet the needs of users. The training needs of more students.
Convenient experiment production tools: Allow teachers to easily mix and arrange texts, pictures, audios, videos, hyperlinks, etc. in different formats such as pdf, ppt, word, and excel, and automatically generate dynamic experiment catalogs to achieve different cloud computing training Resources are displayed on the same screen.
Online Q&A to answer students’ questions in a timely manner: The platform provides online questions for experiments. During the training process, students can communicate with teachers in a timely manner through online Q&A to improve learning efficiency.
Command detection, real-time experiment progress: The platform automatically detects the commands entered by users during cloud computing training, and compares them with the experimental documents to realize the user's experimental progress for the experiment. Every time a command is entered, the platform It will be detected and then displayed on the experiment page in real time. The teacher's classroom page can also view the experimental progress of each cloud computing experiment of students, so as to control the overall learning progress of students.
Automatic generation of experiment reports: For users' experiment reports, the platform records the user's operations during operation of the cloud host, and then automatically generates an experiment report, which teachers can directly view and give corresponding ratings.
Classroom resource recycling: When users create a classroom for practical training, the platform will select the corresponding hardware configuration of the cloud host for each student, which will occupy the CPU resources and memory resources within the resource pool of the institution. When the practical training has completed At the end, users can release the corresponding CPU and memory resources through the classroom release resources, and the training data and records are still saved.
Experiment notes that can record learning situations: The cloud platform provides users with an experiment note function on the experiment page. Users can record their own notes during the experiment.
Supports public resource courses and is easy for teachers to use: the practical training module can be built with rich practical training resources, including practical training documents and experimental images, which users can use directly.
Personal cloud disk, resources will not be lost: The platform will provide users with cloud disk services. All files in the cloud disk will be separated according to different file types, making it easier for users to view and operate.
The platform supports experiments such as Linux, virtualization technology, OpenStack, docker, cloud platform, cloud data center construction and operation and maintenance, cloud storage product configuration, big data platform and big data analysis, and cloud security product configuration.
The platform supports integrated online software development environment, which can be used out of the box, reducing the trouble of users switching back and forth and improving user experience.
The platform can be seamlessly combined with practical modules such as teaching modules, examination modules, homework modules, skills competition modules, artificial intelligence, computer network simulation, Internet of Things, Web front-end, java and python development, etc. to complete the whole process of teaching.
3.2 Artificial Intelligence and Cloud Computing Teaching Cloud Platform
The platform is based on the spring cloud microservice architecture, provides convenient SSO single sign-on, and uses kubernetes for deployment. It can support public cloud, hybrid cloud, and private cloud installation modes. The data layer uses MySQL cluster and MongoDB cluster to realize full-process EdvOps automation. Operation and maintenance has the characteristics of high cohesion, loose coupling, single business, high performance, high concurrency, high possibility, cross-platform, and cross-language. The main modules include course creation tools, cloud disks, shared courses, my courses, cloud preferred courses, cloud video library, and 3D model library.
Course production tools: The platform provides dedicated microservice modules for support, using websocket two-way communication technology, and the underlying storage adopts a three-layer progressive caching method in order to speed up the loading of course resources. Independently develop video transcoding and online video editing functions. It supports direct import from word documents and automatically generates a table of contents based on the title type, which is convenient and fast. At the same time, it supports the insertion of ppt, excel, pictures, hyperlinks, videos, audios, 3D models, chapter tests and other content to realize the same-screen display of multiple hypertext files.
Shared courses: Use the concept of order distribution or campus sharing to share course resources to a greater extent.
My Class: Supports "generating a copy" directly from shared class resources and importing them into My Class, and also supports self-creation. All course resources support the export function and can be exported to local offline files. The exported files are encrypted files with the suffix wz. The course resources can be directly generated by secondary import using the platform to facilitate online dissemination.
Cloud Selected Courses: Learning resources collected and organized on the Internet by senior industry practitioners, including a series of learning videos and knowledge point learning videos for teachers and students to learn independently.
Cloud video library: The platform provides hundreds of micro-lecture videos covering various majors, which can be directly referenced into course resources.
3D model library: Using three.js technology to load 3D models online, providing a more intuitive teaching experience.
The platform can be seamlessly combined with examination modules, homework modules, skills competition modules, artificial intelligence, cloud computing, big data, software development and other practical modules to fully complete the teaching of computer network professional groups.
3.3 Python basic teaching resource package
Chapter 1 Basic Grammar;
Chapter 2 Functions;
Chapter 3 File Operation;
Chapter 4 Exception Handling;
Chapter 5 Modules and Packages;
Chapter 6 Object Oriented;
Chapter 7 Network Programming;
Chapter 8 Regular Expressions;
Chapter 9 XML and Json.
3.4 Docker Getting Started and Practical Teaching Resource Package
Chapter 1 Docker and Containers;
Chapter 2 Core Concepts and Installation Configuration;
Chapter 3 Using Docker Images;
Chapter 4 Operating Docker Containers;
Chapter 5: Accessing the Docker repository;
Chapter 6 uses Dockerfile to create images;
Chapter 7 Using Docker API;
Chapter 8 Core Implementation Technology;
Chapter 9 Configure private warehouse;
Chapter 10 Security Protection and Configuration;
Chapter 11 Docker Machine;
Chapter 12 Docker Compose;
Chapter 13 Docker Swarm;
Chapter 14 Cluster Resource Scheduling Platform—Mesos;
Chapter 15: Production-grade container cluster platform—Kubernetes;
Chapter 16 Other related projects;
Chapter 17 Network Basic Configuration;
Chapter 18 Advanced Network Configuration.
3.5OpenStack Getting Started and Practical Teaching Resource Package
Chapter 1 The concept and development of cloud computing;
Chapter 2 CentOS basic environment configuration;
Chapter 3 Basic operations of data in MySQL database;
Chapter 4: Project development knowledge and skills training;
Chapter 5 OpenStack basic configuration;
Chapter 6 Installing OpenStack services;
Chapter 7 OpenStack daily operation and maintenance;
Chapter 8 Comprehensive Cases.
3.6 Software development training resource package
C language course; Web development basic course; Java programming course; SQLSERVER database course; JavaWeb application design course.
3.7 Cloud Computing Basic Training Resource Package
Practical training resources include:
Web design courses; Java programming courses; MySQL database courses; Linux network operating system courses; Python programming courses; JavaWeb application design courses; Cloud computing comprehensive operation and maintenance management courses; Cloud storage technology courses.
3.8 java programming resource package
Practical training resources include:
Experiment 1 Know Java;
Experiment 2 Java language foundation;
Experiment 3 Java operators;
Experiment 4 Java control statement;
Experiment 5 Java array;
Experiment 6 Java method;
Experiment 7 Java classes and objects;
Experiment 8 Java encapsulation and inheritance;
Experiment 9 Java polymorphism;
Experiment 10 singleton mode;
Experiment 11 string and packaging class;
Experiment 12 error handling;
Experiment 13 enumeration and generics;
Experiment 14 Java collection framework;
Experiment 15 java.io package - character stream;
Experiment 16 java.io package - byte stream;
Experiment 17 Know JDBC;
Experiment 18 JDBC Basics;
Experiment 19 JDBC interface;
Experiment 20 JDBC result set;
Experiment 21 JDBC data types and transactions;
Experiment 22 JDBC exception handling.
3.9 Linux operating system training resource package
Practical training resources include:
Experiment 1 Linux startup, login and exit;
Experiment 2: Practical training on common Linux commands;
Experiment 3: Be proficient in the use of vi editor;
Experiment 4 Linux package management;
Experiment 5 Understand the basic concepts of users and groups;
Experiment 6 Understand user configuration files and master user management commands;
Experiment 7: Understand group configuration files and master group management commands;
Experiment 8 Understand disk partitions and file systems;
Experiment 9 Disk quota management;
Experiment 10 Management of logical volumes LVM;
Experiment 11: Familiar with relevant network configuration files;
Experiment 12 Basic network configuration commands;
Experiment 13: Familiar with network test commands;
Experiment 14 Understand the principles of DHCP;
Experiment 15 Configure DHCP server;
Experiment 16 Configure DHCP client;
Experiment 17 Understand the domain name space and DNS principles;
Experiment 18 Install DNS software and understand DNS configuration files;
Experiment 19 DNS server configuration;
Experiment 20 Configure vsftpd server;
Experiment 21 Client accesses FTP server;
Experiment 22 Understand the working principles of WWW services and Web services;
3.10 MySQL training resource package
3.11 Python programming training resource package
Practical training resources include:
Experiment 1 Python overview;
Experiment 2 Python’s simple data types;
Experiment 3 python advanced data types;
Experiment 4 Python program structure;
Experiment 5 Python function;
Experiment 6 Python object-oriented;
Experiment 7 Python file operation;
Experiment 8 Python exceptions, debugging, and testing;
Experiment 9 Python network programming;
Experiment 10 Python regular expressions;
Experiment 11 XML and json.
4. Artificial Intelligence and Cloud Computing System Training Room Construction Diagram
5. Artificial Intelligence and Cloud Computing System Training Room Plan List
6. Value of Artificial Intelligence and Cloud Computing System Training Room Program
6.1 Professional teaching support
6.2 1+X authentication service
6.2.1 Cloud computing development and operation and maintenance 1+X certificate
6.2.2 Cloud Computing Application Development 1+X Certificate
6.3 Skills competition support
6.3.1 Cloud computing technology and application
7. Action plan for co-educating digital talents between schools and enterprises based on Huawei’s ecological ecosystem
