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Course Catalog Overview Four Tracks · Architect Level

Four Tracks
Covering the Cloud & AI EraIn-Demand Capabilities

From Linux infrastructure to Go cloud-native, from DevOps/K8s to AI LLM operations

Every course is a complete growth path aligned with top-tier expert standards

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Core Courses

Architect Career Program · Four Tracks

Four tracks covering the most in-demand architect skill models in the Cloud & AI era.

Linux Cloud Computing

Linux Cloud Computing SRE Architect Program

Solid Foundation · Deep dive into principles and core concepts
Framework advancement and source code analysis
Automation mindset and IaC
Enterprise project performance optimization hands-on
Full-stack capability expansion

Duration: 4-5 months Mode: Live + Replay For: Beginners / Career switchers / Upskillers

View Full Syllabus

DevOps / K8s

DevOps Microservices K8s Architect Program

Java core concepts and JVM internals
Java application development ecosystem
Middleware selection and architecture design
Distributed systems architecture principles
Big data engineering and ecosystem

Duration: 4-5 months Mode: Live + Replay For: Those with Linux/dev background

View Course Details

Go / Cloud-Native / AI

Go Development & Cloud-Native Architect Program

Go core concepts and concurrency model
Cloud-native architecture and microservices
AIGC technology application scenarios
Prompt engineering and toolchain
Building AIGC application tools

Duration: 4-5 months Mode: Live + Replay For: Those with programming background

View Course Details

AI LLM Operations

AIOps Intelligent Operations with LLM Premium Class

LLM fundamentals and Ollama hands-on
DeepSeek / model selection hands-on
Enterprise private deployment
Intelligent Agent & RAG
AIOps intelligent operations hub

Duration: 3-4 months Mode: Live + Replay For: Ops/dev advancement

View Full Syllabus

Linux Cloud Computing SRE · Full Syllabus

Seven stages, from beginner to high salary

Click each stage to expand detailed syllabus. Each stage contains 3 modules and 20+ core knowledge points.

Stage 1Foundation: Linux Core + AI Ops Intro

Module 1: Linux Ops Foundation & AI Ops Awareness

1. Learning the Meaning of Linux: AI and Cloud Core Carriers
2.Linux Introduction Kernel and Distribution Architecture
3.Linux Advantage Open Source Stable Customizable
4.Linux Distribution Enterprise/Community Edition Selection
5. Red Hat Enterprise Benchmarking for AWS/Azure
6.OpenEuler domestic open source Ronghua cloud
7.Ubuntu Cloud Native AI Development Preferred
8. SUSE Financial Grade Stable Multi-Cloud Management
9.Fedora New Technology AI Tool Adaptation
10.Rocky Linux compatible with Red Hat
11.32/64 Bit Difference: Impacting AI and Cloud Processing
12.Linux Kernel Naming Association AIOps Cloud Compatibility
13. Cloud Core Linux in IaaS/PaaS/SaaS Role
14.Linux Outlook 2026 Salary Trends
15.Windows and Linux AIOps division of labor
16.AI O&M defines technology stack value
17.AIvs Traditional O&M from passive to proactive
18. AI operation and maintenance prospects large factory demand landing path
19.AIOps scenario anomaly detection and other cases
20. Battle Cloud AIOps Configuration Server Initialization
21. The AI Agent is pre-installed on the actual cloud Linux instance

Module 2: Linux System Deployment & First AI-Assisted Ops Experience

1.Linux Installation Preparation Physical Machine Cloud Service Differences
2. Enterprise Linux installation cloud host image production
3. Learn Linux Skills: AI-Assisted Command Troubleshooting
4. System startup cloud environment process optimization
5.BIOS/UEFI Affects Cloud AIOps Deployment Compatibility
6.MBR Limited GPT Alternative
7.GPT Partition Table Cloud Clothing AIOps Advantage
8.GRUB Boot Configuration Cloud Failure Repair
9.Linux boot process BIOS to login
10. Load BIOS Cloud Virtualization Adaptation
11. Read MBR/GPT cloud storage mount
12.GRUB Bootstrap AI Log Assisted Troubleshooting
13. Load Kernel Cloud Performance Optimization
14. Run level systemd management
15. Load rc.sysinit cloud monitoring startup
16. Load the kernel module AI acceleration card configuration
17. Launcher AI Agent Self-launch
18. Read rc.local Cloud Resource Initialization
19. Enforce login cloud security compliance
20.NetworkManager docking cloud monitoring
21. NMCLI mass allocation cloud service network
22.TCP/IP protocol AIOps traffic metrics
23.IP Common Sense Cloud VPC Planning Assignment
24.IP Classification CIDR Cloud Web Applications
25. Gateway/Mac cloud routing fault location
26.Linux with IP Linkage DHCP Cloud Service
27.Linux with DNS docking cloud resolution optimization
28. Network card naming adaptation cloud asset management
29.Linux Password Reset Cloud Server No Console Scenario
30. Remote management SSH configuration AI batch login
31. Actual DeepSeek Assisted grub Failure Repair
32.AI Tools Deployment Ollama + DeepSeek Localization

Module 3: Linux Core Commands & AI Productivity Tools Hands-on

1.Linux Directory FHS Standard Cloud Environment Planning
2. AIOps Perspective Log Configuration Model Storage Specification
3. Cloud storage mount object/block storage permission configuration
4. Basic commands cd/ls/pwd core operations
5. File command mkdir/rm/cp/mv combat
6. Log command cat/head/tail AI extraction exception
7. Compression command zip/gzip/tar cloud archive AI compression
8. System command df/du/free to interface with cloud monitoring
9. Disk command fdisk/parted cloud hard disk operation
10. Network command ping/ssh multi-cloud management
11.diff command cloud configuration change detection
12.vim editor mode switching operation and maintenance skills
13.vim advanced macro recording batch configuration
14.Linux User Group Cloud Environment Permission Isolation
15. User command useradd/userdel combat
16. Group command groupadd/groupdel application
17. Permission core rwx and digital permission conversion
18.chown permission assignment cloud security compliance
19.chmod symbol digital permission battle
20. Case 1 Grant user jfedu.net rwx permission
21. Case 2 Granting Group jfedu.net Permissions and Revocation
22. Case 3jfedu.net Permissions Batch Management AI Audit
23. Special Permissions SUID/SGID Cloud Security Risk
24.umask default permissions cloud server initialization configuration
25.find command cloud server abnormal file positioning
26. Practical AnythingLLM build command knowledge base
27. Real-world Ollama generates cloud inspection scripts
28. Command anomaly AI combined with cloud monitoring root cause analysis

Stage 2Core Services Hands-on: Databases / Middleware + AI Intelligent Ops

Module 1: MySQL Database Ops & AI Empowerment

1. Introduction to MySQL Cloud Native AI O&M Trends
2. Database Comparison Self-built MySQLvs Cloud RDS
3.MySQL/MariaDB Deploy yum/Binary
4.MySQL source code deployment 5.7 compilation optimization
5. MySQL8 Binary Deployment Configuration Planning
6.Mariadb11 Binary Deployment Cloud Adaptation
7. Cloud MySQL Deployment Security Group Backup High Availability
8. MySQL core command addition, deletion and modification transaction
9. Character set setting UTF8mb4 encoding resolution
10. Password Management Modification Reset Cloud Environment Adaptation
11. Granular control of cloud security by user authorization
12.my.cnf Core Parameter Performance Optimization
13. Index Case B + Tree Query Optimization
14. Slow query log opening analysis optimization
15. Command-line tricks for bulk operation of cloud storage exports
16.MySQL optimized index query configuration 3D scheme
17.AIOps App DeepSeek Optimizes SQL Indexes
18. Practical AI Monitoring MySQL Slow Query Generation Scheme
19. Master-slave replication principle binary log synchronization
20.MySQL8 master-slave configuration cloud cross-regional deployment
21. Master-Slave Fault AI Auxiliary Log Diagnosis
22. Master-slave repair ignore error resynchronization
23.MySQL Cluster High Availability Docking Cloud Load
24. Advanced LLM Build Fault Knowledge Base Intelligent Q&A
25. Practical AI preprocessing data storage MySQL support analysis

Module 2: Redis Caching & MyCAT Middleware + AI Management

1.Redis7 Introduction In-Memory Database Cloud Cache Value
2. Cache vs. self-built Redisvs cloud cache
3.LAMP+Redis Session Sharing Cache Case
4. PHP interacts with Redis
5. Redis Configuration Core Parameters Memory Optimization
6. Persistent RDB/AOF combined with cloud backup
7. Redis Master Slave Cluster Data Redundancy Failover
8. Redis Cluster Three Master and Three Slave Deployment Verification
9.Redis Cluster hash slot communication mechanism
10. Backup and recovery cloud storage solutions in action
11.LNMP read-write separation Redis cache acceleration
12.MyCAT Introduces Database Middleware Value
13.MyCAT Multi-Role Perspective Focus
14.MyCAT Principle Library Table Solution
15. MyCAT Core Logic Library Table Fragmentation Table
16. Fragmentation Rule Cloud Database Subtable Adaptation
17. Multi-Tenancy Isolation/Shared Library Isolation
18. Advantages and disadvantages of vertical and horizontal data segmentation
19. Data Split Principle Data Source Management
20.MyCAT2 Deployment Cloud Host Base Configuration
21. MyCAT read-write separation test synchronization verification
22.MyCAT Management Commands Repository Table Heartbeat
23.AIOps Applies DeepSeek Design Sharding Rules
24. Practical AI monitoring Redis memory generation strategy
25. Actual MySQL data AI analysis output build table

Module 3: Web Service Architecture & AI Protection Optimization

1. Introduction to Nginx How It Works Event Driven
2.Web Architecture Nginx + Cloud Load + CDN
3.Nginx Deployment Binary Source Cloud Installation
4. Nginx Module Access/Auth_basic, etc.
5. Current limiting configuration Limit_rate combined with cloud current limiting
6. Nginx Configuration Performance Secure Cloud Adaptation
7. Virtual Host Domain Name Port IP Configuration
8.Location matching rule priority combat
9.Rewrite Rule URL Rewrite Jump
10. Practical Nginx Motion-Static Separation LNMP Cloud Deployment
11.LNMP configuration MySQL/PHP/Nginx linkage
12.Nginx Log Custom Cutting to Cloud Storage
13. Anti-theft chain Referer configuration cloud resource protection
14.HTTPS Configure SSL Docking Cloud Certificate
15.Tomcat10 Deployment Configuration
16.Tomcat configuration Server.xml core parameters
17. Connector bio/NiO/Apr performance comparison
18.JVM Detailed Stack Memory Model Optimization
19. Tomcat Optimized JVM Parameter Thread Pool
20.HTTP protocol request response header status code
21. Impact of HTTP 1.1 Long Connections on Nginx
22.AIOps applies AI to optimize nginx routing
23.AI Assisted Nginx Defense DDoS Anomaly Blocking
24. Practical AI Analysis Nginx Performance Generation Scheme
25. Practical LLM Generation LNMP Cloud Deployment Script

Stage 3Monitoring & Logging: Distributed Monitoring + AI Anomaly Detection

Module 1: Zabbix/Prometheus & AI Integration

1. Monitoring system Cloud monitoring + open source monitoring collaboration
2.Zabbix7 Introduction to Core Components Process
3. Zabbix Acquisition Agent/SNMP/JMX
4. Zabbix Deployment Server/Proxy Cloud Span
5.zabbix_server.conf Core Parameters
6. Zabbix web initialization interface settings
7. Zabbix Agent Active Passive Mode
8. Zabbix Proxy Distributed Monitoring Configuration
9. Zabbix docking cloud monitoring data synchronization
10. Zabbix Automatic Discovery Cloud Host Configuration
11. Alarm configuration email WeChat cloud message
12. Zabbix monitors MySQL master-slave triggers
13. Advanced macro batch configuration dynamic parameters
14. Monitor prototype bulk port service availability
15. Monitor website keywords business availability
16. Zabbix Common Troubleshooting Ideas
17.Zabbix Trigger Script Failure Automatic Repair
18.Prometheus+Grafana Cloud Native Deployment
19.Prometheus Core Indicator Data Model
20.Prometheus docking cloud monitoring API aggregation
21. Grafana Visual Dashboard Making
22.AIOps integration Zabbix anomaly AI analysis
23.AI Abnormality Detection Threshold Adaptive
24. Actual Arima predicts CPU linkage cloud expansion
25. Smart Alarm Zabbix Noise Reduction Merge
26. Practical Zabbix data training AI detection model

Module 2: ELFK Log Analysis & AI Intelligent Diagnosis

1.ELK7 Architecture Components Collaborative Data Flow
2.ELK Process Acquisition-Filter-Storage-Visualization
3.Redis Buffer Elk High Concurrency Optimization
4.ELFK Process Filebeat Lightweight Acquisition
5. Cloud Elk deployment single node vs. cluster scaling
6.ES7 Configure User Rights Cluster Settings
7.ES plugin IK word breaker monitoring plugin
8.ES docking cloud storage backup recovery
9.ES Fault Cluster Crack Recovery Drill
10.Kibana7 Chinese Settings Access Control
11. Kibana Visual Indexing Dashboard
12.Kibana docking cloud monitoring panel
13.Kibana Security X-PACK Certification
14.Logstash7 JDK Configuration Binary Deployment
15.Logstash Plugin Input/Filter/Output
16.Grok syntax log structured extraction
17. Logstash indexes are managed by time partition
18.Filebeat collects Nginx/Tomcat logs
19.Filebeat multi-log source custom index
20.ELK collection system Multi-class logs such as MySQL
21.ELK Analysis Peak Visits Identification
22.Redis Accelerated Elk Log Cache Peak
23.AIOps App DeepSeek Parsing Failure Logs
24. Arima predicts ES data trends
25. Practical Kibana + AI traffic prediction alarm
26. Practical AI draws Kibana traffic forecast curve

Stage 4Automation & Security: Shell Programming + AI Ops Development

Module 1: Shell Programming Advanced & AI-Assisted Development

1. Shell starter script execution interpreter
2. Shell Hello World script permissions
3. Shell Variables System Environment User Variables
4. Shell Symbol Quotation Bracket Redirect
5. If Conditional Integer String File Judgment
6.If Multi-conditional Score Rating Logic
7.Case Statement Menu Interaction Branch
8.Select statement interactive menu
9.For loop number list bulk operation
10.For loop cloud server file transfer command
11.For Circular Log Packaging User Creation
12.While loop condition infinite progressive read
13.While Loop File Monitoring Login Monitoring
14.Shell function judge_ip wrapper
15.Find by name type size permission
16.SED Text Replace Delete Insert
17.AWK field handling built-in variables
18.GREP Text Filter Regular Match
19. Shell array definition access substitution deletion
20. Cloud script shell calls API to start and stop the server
21. System script backup information collection
22. Deployment script lamp/LNMP one-click configuration
23. Database script MySQL master-slave backup
24. Monitoring Scripts Nginx Logs Disk Monitoring
25. Security scripts block malicious IPs
26.AIOps Assisted LLM Generation of Inspection Scripts
27.Shell preprocessing AI training data
28. Practical AI Prediction Driven Shell Cloud Resource Adjustment
29. Practical Shell Deployment Management AI Model

Module 2: Automation Tools + AI Batch Processing

1. Cloud Automation Tool Selection Architecture Design
2. Tools vs. Puppet/Saltstack/Ansible
3. Ansible Principle No Agent SSH Communication
4. Ansible Installation yum Configuration Explained in Detail
5.Inventory Host Inventory Cloud Dynamic Update
6.Ansible core module ping/copy, etc.
7. Command module command/shell/script
8. File module copy/file/synchronize
9. Package management yum/pkg installation upgrade
10. User module user/group management
11. Scheduled Task Module cron Configuration
12.Playbook YAML syntax component
13.Playbook Variable Template Bulk Deployment
14. Ansible calls API to build cloud servers in batches
15.Ansible optimizes SSH concurrent cloud adaptation
16.Ansible Accelerated Close Key Detection
17. Saltstack Introduction C/S ZeroMQ Communication
18.Saltstack Deployment Master/Minion
19.Salt Node Hosts Firewall Configuration
20.Salt core module ping/cmd, etc.
21.Salt State SLS Case Study
22.SLS Case Nginx/Tomcat Deployment
23.AIOps App DeepSeek Writes Ansible Script
24.AI+Ansible Fault Prediction Auto Repair

Module 3: Linux Security Attack/Defense & AI Protection Practice

1. Cloud security system Cloud host network protection
2.TCP/IP Header AIOps Traffic Analysis
3. TCP handshake and waving connection mechanism
4.DDOS attack SYN Flood/CC, etc.
5. Syn Flood Defense Kernel Optimized Cloud Linkage
6. CC attack principle defense strategy
7.HTTP Flood Defense Nginx Throttling Cloud WAF
9.Hydra Brute Force SSH/MySQL Defense
10.Libssh installation source yum method
11.Hydra Case Defense Log Audit
12.Metasploit Penetration Component Deployment
13.Msfconsole penetrates MySQL/Tomcat
14. DenyHosts anti-violent cracking email alarm
15.DenyHosts Manage IP Delete Cloud Adaptation
16. IPtables Bracelet Filter/NAT, etc.
17. IPtables Process Forwarding Rule Sequence
18. Add, delete, modify, and save IPtables commands
19.IPtables Case Web Database Protection
20.Firewalld Area Management Command Actual
21. Firewalld Configuration Persistent Rule
22.Linux Security Password Sudo Port Control
23. Cloud Host Baseline AI Automation Audit
24. Shell scripts block abnormal IPs
25.AIOps App DeepSeek Generate Defense Rules
26. Combat AI-powered DenyHosts Linked Cloud Security Team

Stage 5Virtualization & Cloud-Native: Docker/K8s + AI Intelligent Scheduling

Module 1: Docker Virtualization & AI Management Platform

1. Virtualization Overview VMware/KVM/Docker Cloud Relationship
2. Comparison of virtualization technology types
3. Docker Advantage Cloud Native Adaptability
4. Docker Schema Client/daemon/containerd
5. Core Concept Mirroring Container Warehouse Lifecycle
6. Cloud Docker Deployment Installation Domestic Source Configuration
7. Enterprise Docker Security Configuration Resource Limits
8. Core command search/pull/run/exec
9. Container management stop/start/restart/rm
10. Network Mode Bridge/Host/Container
11. Bridge Mode Container Communication Cloud Adaptation
12. Data Volume Local Cirrus Storage Mount
13.Dockerfile from/run/copy and other commands
14. Dockerfile specification hierarchical optimization
15.Dockerfile Case Nginx/MySQL Image
16. Warehouse Management Docker Hub/Registry/Harbor
17. Harbor Deployment Cloud Environment Image Management
18.Docker monitoring stats docking cloud monitoring
19. Resource Limit Disk Memory CPU Configuration
20. Consistent environment for rapid deployment of Docker AI models
21.AI drives dynamic allocation of Docker resources
22.AIOps App DeepSeek Detects Mirroring Vulnerabilities
23. Practical AI-driven Docker Cloud Elastic Scaling
24. Advanced Docker Deployment LLM Service Cloud Adaptation

Module 2: Kubernetes Cloud-Native & AI Ops Practice

1. Cloud Computing and K8s Core Values Cloud Native Cornerstone
2. Cloud-native microservices work with K8s
3. K8s Component Control Plane Node Component
4. Cloud K8s deployment self-built vs hosted EKS/ACK
5. Core Resources Pod/Label/Replication Controller
6. Core Resources Service/Node/Volume
7.Volume type local cloud storage docking
8.K8s Deploy Kubeadm Build Master/Node
9.K8s Node Hosts Firewall Configuration
10. Kernel parameter K8s node performance optimization
11.K8s Network Flannel/Calico Cloud VPC Adaptation
12. Private repository image pull configuration
13.Service ClusterIP/NodePort
14.Service Case Internal Communication External Access
15.Dashboard Deployment Permission Configuration
16.K8s Failure Etcd/pod/Docker Issues
17. Hosted K8s ACK/EKS cluster application deployment
18.AI-driven K8s resource load-aware scheduling
19.AI helps K8s fault prediction and self-healing
20.AIOps application AI-driven K8s resource scheduling
21. Practical AI monitoring K8s pod failure recovery program
22. Advanced K8s performance monitoring AI optimization

Stage 6Advanced AI Ops: LLM Development & Enterprise Practice

Module 1: LLM Knowledge Base & Model Fine-tuning Practice

1. AIOps core LLM cloud operation and maintenance landing scene
2. Large model deployment GPU cloud host resource configuration
3. AnythingLLM Deployment O&M Cloud Knowledge Base
4.Ollama Manages DeepSeek/LLaMA Cloud Adaptation
5. Model fine-tuning operation and maintenance cloud monitoring data preparation
6.Linux Package Management rpm/Tar/yum/Source Code
7.RPM management installation query uninstallation
8.Tar command parameter system backup
9.YUM Principle Local Network Cloud Source Configuration
10.YUM Case Priority ISO Local Source
11. Synchronize Extranet yum Source Extension
12. Introduction to Hard Drives Block and Inode
13. Hard and Soft Links Differentiate between Enterprise Applications
14. AI-assisted diagnostic repair of hard drive failures
15. The actual AI automatically configures the yum source
16. Actual AI Hard Disk Test Generation Report
17. Practical AI Collection High Frequency Linux Command Optimization Learning
18. LLM API Development Docking O&M Cloud Platform
19. Battle Cloud LLM High Availability Docker + K8s Deployment

Module 2: AI Ops Agent Development & Enterprise Deployment

1. AI Agent Principle Cloud Operation and Maintenance Scenario Design
2. Agent development environment cloud host configuration
3.CI/CD Concepts Legacy vs Continuous Integration Differences
4. Jenkins introduces core component values
5. Jenkins Deploys War Package Docker Method
6.Jenkins Concept Building/job/Plugins
7. Compile tool vs. Make/Ant/Maven
8. Jenkins job source code extraction and construction
9.Jenkins Automation Plugin Script Integration
10. Jenkins Mail Multi-Instance Configuration
11. Jenkins + Ansible High Concurrency Deployment
12. Version control svn and Git differentiation selection
13.SVN Deploy yum/Source + Apache
14. SVN Client Checkout Submission Branch
15.Git Deployment yum/Source Configuration
16.Git repository local remote interaction
17.Git command add/commit/push/pull
18. Actual 1 Cloud Resource Inspection Agent Development
19. Actual 2 Database Intelligent Operation and Maintenance Agent Development
20.AI predicts Jenkins build failure
21.AI Analysis Jenkins Data Optimization Process
22.AIOps Integration Agent for Zabbix/elk
23. Practical AI Prediction Driven Shell Cloud Resource Adjustment

Stage 7Career Advancement: High-Salary Interviews & Enterprise Projects

Module 1: Enterprise Comprehensive Project Practice

1. Keepalived VRRP Principle for Highly Available Clusters
2.Nginx+Keepalived master-slave dual master architecture
3.Redis+Keepalived data consistency
4.MySQL+Keepalived master-slave switching
5. Haproxy Introduction to Load Algorithm Scenarios
6.Haproxy+Keepalived configuration test
7.LVS Introduction Working Mode Principle
8.LVS+Keepalived DR mode configuration troubleshooting
9. Project 100 million PV cloud architecture AIOps monitoring
10. Architecture Design Cloud Load + Nginx + K8s + Cloud Database
11. Implement Nginx + K8s cloud deployment
12. Implement Redis Cluster + MySQL master-slave span
13. Implement elk + Zabbix cloud monitoring linkage
14. Optimize AI-driven elastic scaling of cloud resources
15. Project 2 Enterprise AIOps Platform Construction
16. Project 3 Cloud Native Application AI Operation and Maintenance
17. Project 4 Cross-Platform Automation Ansible + Jenkins
18. Project 4 Optimizing AI Forecasting Build Risk Costs

Module 2: High-Salary Job Search & Interview Mastery

1. AI O&M Capability Model Linux + AI + Cloud
2. Job requirements analysis JD analysis of large factories
3. Select the company's development prospects and match the positions
4. Advantages and disadvantages of large-scale vs small-scale factories of the company
5. The city chooses first-line vs. new first-line opportunities
6. Inexperienced job search project packaging ability display
7. Age Gender O&M Job Search Skills
8. Resume optimization highlights Linux + AI + cloud projects
9. Resume common sense HR screening logic to avoid pitfalls
10. Work experience star rule outcome quantification
11. In-depth business value of project packaging technology
12. Personal work matches recruitment needs
13. Interview Must Ask Core Knowledge Points 3000 + Question Bank
14. Cloud-Native AI Operation and Maintenance Solution for Interviews
15. Interview Skills Self-introduction Project Explanation
16. Effective communication skills for technical interviews
17. Career Planning Development Pathway CKA/AWS Certification
18. Mock Interview High Frequency Question Answer
19.offer negotiation onboarding preparation

AIOps AI Architect · Full Syllabus

Eight stages, from LLM to intelligent operations hub

Click each stage to expand detailed syllabus, covering LLM deployment, fine-tuning, Agent, RAG to enterprise AI hub.

Stage 1LLM Fundamentals & Ollama Hands-on

1.1 Core Concepts and Values of the Large Model
1.2 Large model parameters and technical essence
1.3 Underlying Principles of Transformer Architecture
1.4 Large model data training and computing power logic
1.5 Why big models have to rely on GPUs
1.6 Ollama Command Line Basic Operations
1.7 Ollama Model Starting and Stopping the Whole Process
1.8 Ollama Interactive Dialogue and Functional Testing
1.9 Ollama Model Download and Version Management
1.10 Ollama Offline Deployment: Dependencies and Installation Steps
1.11 Ollama Online Deployment: Network and Version Configuration
1.12 Ollama Deployment Validation and Service Status Check
1.13 Ollama Common Deployment Issues and Solutions
1.14 Ollama Profiles and Custom Running Rules
1.15 Ollama CPU/GPU Resource Limitation and Control
1.16 Ollama Log Analysis and Troubleshooting
1.17 Ollama API interface actual call
1.18 Ollama High Availability Deployment Scenario
1.19 Ollama and AIOps Scenario Adaptation Benefits

Stage 2Model Selection & DeepSeek Hands-on

2.1 Ecology and Characteristics of Open Source Large Model
2.2 Ecology and characteristics of closed-source large models
2.3 Core Dimensions of Enterprise-Level Large Model Selection
2.4 Scenario Model Selection Strategies for Different Industries
2.5 DeepSeek Model Deployment and Validation Steps
2.6 DeepSeek Q&A Function Practical Testing
2.7 DeepSeek Service Status Viewing and Monitoring
2.8 DeepSeek Scenarios for Underresourcing
2.9 DeepSeek Startup Parameters and Performance Tuning
2.10 DeepSeek API Interface Battlefield Integration
2.11 DeepSeek Interfaces with Enterprise Knowledge Base
2.12 Docker Containerized Deployment DeepSeek Scenario
2.13 Ollama Deploy DeepSeek Complete Process
2.14 Application of integrated model deployment in AIOps

Stage 3Enterprise LLM Private Deployment & Hardware Architecture Design

3.1 Hardware Configuration Estimation
- Hardware Core Configuration Logic
- Quantization Technology and Graphics Memory Optimization
- Typical budget scenario
- Graphics Memory Estimation Formula
3.2 Hardware Selection Strategy
- Demand-driven decision making
- Typical Enterprise Scenario Cases
3.3 Deployment of large open source models
- Deployment Ideas and Scenarios
- Alibaba Cloud Pai One Click Deployment
- Tencent Cloud Hai Experience
- ollama deployment
- VLLM deployment
- Cluster Mode Deployment

Stage 4LLM Fine-tuning Optimization & Enterprise Custom Models

4.1 Understand the fine-tuning of large models
- Why fine-tuning is needed
- Fine-tuning technical classification
- Technical Options Guide
- Fine-tune strategy
4.2 Large model fine-tuning tool
- Open source fine-tuning tool
- Commercial fine-tuning/fine-tuning platform
4.3 Large model fine-tuning datasets
- Fine-tuning dataset classification
- Dataset Format
- Get a public dataset
- Create your own dataset
4.4 Fine-Tuning Hyperparameters
4.5 Large model fine-tuning actual combat
- Xunfei Spark fine-tuning real combat
- LLaMA-Factory trimmed Qwen3 large model
- Unsloth trimmed Qwen3 large model

Stage 5Intelligent Agent System Building & Automated Ops Practice

5.1 Theoretical basis of the agent
5.2 Coze Agent Platform
- 5.2.1 Quickly experience the Coze agent
- 5.2.2 Workflow Actuals
- 5.2.3 Dialogue flow combat
- 5.2.4 Knowledge base combat
- 5.2.5 Database combat
5.3 Actual landing based on Coze agent platform
- 5.3.1 Creating a Workflow
- 5.3.2 Creating an Agent
5.4 Building an agent based on the open source platform Dify
- 5.4.1 Meet Dify
- 5.4.2 On-premises Dify
- 5.4.3 Configuring the Model in Dify
- 5.4.4 Configuring Plugins in Dify
- 5.4.5 Creating a Chatfow App
- 5.4.6 Creating a Workflow
- 5.4.7 Creating a Knowledge Base
- 5.4.8 Dify agent combat
5.5 Agent combat based on MCP (Alibaba Cloud Refining)
- 5.5.1 MCP Basics
- 5.5.2 Quickly experience Alibaba Refining MCP
- 5.5.3 Make a tourism plan based on Alibaba Cloud Refining MCP
5.6 Private deployment of the open source version of Coze
- 5.6.1 Preparing the Linux Machine
- 5.6.2 Installing Docker and Docker-compose
- 5.6.3 Clone Source
- 5.6.4 Running coze
- 5.6.5 Using coze
5.7 n8n based agent combat
- 5.7.1 Introduction to N8n
- 5.7.2 N8n Deployment
- 5.7.2.1 Preparations
- 5.7.2.2 Installation of n8n
- 5.7.3 Experience n8n
- 5.7.3.1 Create a workflow based on a template
- 5.7.3.2 Custom Workflows
- 5.7.3.3 Introduction of n8n nodes
- 5.7.3.3.1 Trigger Node
- 5.7.3.3.2 File Operations Node
- 5.7.3.3.3 Control nodes
- 5.7.3.3.4 Loops and Iterations
- 5.7.3.3.5 Consolidation
- 5.7.3.3.6 Process control
- 5.7.3.3.7 code node
- 5.7.3.3.8 Data nodes
- 5.7.3.3.9 Storage Nodes
- 5.7.3.3.10 Tripartite storage
- 5.7.4 Building an Agent with n8n

Stage 6RAG Retrieval Augmentation & Enterprise Knowledge Base Implementation

6.1 rag Foundation
6.2 Vector Database Milvus
- 6.2.1 Understanding Vector Databases
- 6.2.2 Quick Start Milvus
6.3 Implementation of rag landing based on FastGPT
- 6.3.1 Introduction and Installation of FastGPT
- 6.3.2 Getting Started FastGPT
- 6.3.3 Project Operations
6.4 rag landing based on RAGFlow
- 6.4.1 Understanding RAGFlow
- 6.4.2 Deploying RAGFlow on Linux Machines
- 6.4.3 Experience RAGFlow Quickly
- 6.4.4 Project Operations

Stage 7LLM Ops Monitoring, Performance Tuning & Security Governance

7.1 Monitoring of Large Model Platforms
- 7.1.1 Basic command-line tools
- 7.1.2 Prometheus + Grafana, a professional monitoring tool
7.2 Large Model Optimization
- 7.2.1 Optimization Strategies
- 7.2.2 Large Model Quantification
- 7.2.3 Large Model Knowledge Distillation
- 7.2.3.1 Core mechanisms of knowledge distillation
- 7.2.3.2 Technical method classification of knowledge distillation
- 7.2.3.3 Baidu Intelligent Cloud Qianfan Large Model Platform Large Model Distillation
- 7.2.3.4 Distillation of large models with DistillKit
7.3 Large Model Pressure Test
- 7.3.1 Pressure Measurement Indicators
- 7.3.2 Pressure Measurement Tools
- 7.3.2.1 Alibaba Cloud Pai Model Online Service (EAS)
- 7.3.2.2 Baidu Intelligent Cloud Qianfan ModelBuilder
- 7.3.2.3 EvalScope
- 7.3.2.4 Locust
- 7.3.3 Pressure Measurement Actual Combat
7.4 Safety Operation and Maintenance of Large Models

Stage 8AIOps Intelligent Ops Platform & Enterprise AI Hub Practice

8.1 AI-Assisted Programming
- 8.1.1 GLM4.6 Full Stack Development
- 8.1.2 Meaning zero code
- 8.1.3 Trae
- 8.1.4 claude code
8.2 Product Requirements Document Design
8.3 Project Development
- 8.3.1 Landing Requirements with Tongyi Spirit Code
- 8.3.2 Landing Requirements with Codex/Claude Code
- 8.3.3 Cloning a website with AI
8.4 Project Testing and Deployment Launch
- 8.4.1 Registering for an account
- 8.4.2 Pushing Code to github
- 8.4.3 Deploying a Project in Vercel
- 8.4.4 Binding a Domain Name
8.5 Operation and maintenance agent based on Coze
- 8.5.1 Coze Custom Plugins
- 8.5.1.1 API-based plugin creation
- 8.5.1.2 Create custom plugins based on IDE
- 8.5.2 Custom coze plugin manages Alibaba Cloud machines
- 8.5.2.1 Preparation
- 8.5.2.2 Creating the Coze Plugin
- 8.5.3 Design Coze workflows
- 8.5.4 Designing an aiops agent
8.6 Using Coze + Ansible as an automated O&M agent
- 8.6.1 Preparations
- 8.6.1.1 Preparing the Ansible Environment
- 8.6.1.2 Script the ansible api service and turn on the API
- 8.6.1.3 Writing a playbook
- 8.6.2 Creating the coze plugin
- 8.6.3 Creating a coze workflow
- 8.6.4 Configuring the coze agent
8.7 Dify + jumpserver as an O&M agent
- 8.7.1 Deploying Jumpserver
- 8.7.1.1 Deploying a jumpserver
- 8.7.1.2 Quick Jumpserver Experience
- 8.7.2 Deploying a Jumpserver MCP
- 8.7.2.1 Acquisition of user tokens
- 8.7.2.2 Deploying a jumpserver MCP
- 8.7.2.3 Add Jumpserver MCP to Dify
- 8.7.3 Implementing a Simple Requirement
- 8.7.3.1 Creating a Dify App
- 8.7.3.2 Testing the Dify app
- 8.7.4 Doing a Comprehensive Application Agent
8.8 Dify + k8s as an O&M agent
8.9 Dify + Prometheus + Alertmanager as an O&M agent
8.10 Operation and maintenance agent with n8n + Prometheus + Alertmanager
8.11 Using Dify + Ansible's MCP as an O&M agent
8.12 Devops + AIOps agent with n8n + Jenkins
8.13 AI agent enterprise-level operation and maintenance hub battle
- Introducing OpenClaw
- OpenClaw Deployment
- OpenClaw Access Chat Tool
- OpenClaw Multi-Agent Collaboration
- OpenClaw combat
- OpenClaw and AIOps All Scenario Applications
- Anatomy of a Hermes Agent Concept
- Hermes in action
- Hermes access to the chat tool
- Hermes Agent Collaboration
- Hermes Troubleshooting
- Hermes Agent and AIOps All Scenario Applications

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Four TracksCovering the Cloud & AI EraIn-Demand Capabilities