I asked DeepSeek to design an intelligent operation and maintenance platform

The following is the "DeepSeek+ Intelligent Operation and Maintenance Platform" integration solution designed for operation and maintenance engineers, including technical architecture, implementation path and specific scenario applications, which are divided into six core modules and gradually promoted:

1. Technical architecture design

• Collection objects: server logs, monitoring indicators (Prometheus), work order records, CMDB configuration library, network traffic data  

• Technology stack: Fluentd/Filebeat (log collection), Telegraf (metric collection), Kafka (real-time streaming pipeline)

• DeepSeek model deployment:  

    Customized version: Use LoRA to fine-tune data in the operation and maintenance field (requires NVIDIA A100 or higher computing power)  

• Auxiliary components:  

     Time series prediction module (Prophet+DeepSeek joint analysis)

3. Application layer 

•  Core functional modules: intelligent alarm, root cause analysis, plan execution, capacity forecasting, etc.  

•  Execution engine: Ansible/Terraform docking automation tool chain

4. Interaction Layer

• Natural language console: supports voice/text commands such as "query the top 3 servers with nginx error rates"  

• Visualization screen: Grafana integrates AI analysis results

• Pain point: Manually checking massive logs is inefficient and difficult to find hidden patterns  
• DeepSeek Applications:  

  # Log classification example (using the fine-tuned model) def log_analyzer(raw_log): prompt = f""" Please classify the following logs and extract key information: [Log content]{raw_log} Optional categories: hardware failure/application error/network interruption/security attack Output JSON format:{"type":"","error_code":"","affected_service":""} """return deepseek_api(prompt)

• Real-time labeling of abnormal logs (accuracy increased by 40%+)  
• Automatically generate an "Event Analysis Report" (including timeline graph and repair suggestions)

• Scenario: When the MySQL master-slave delay is detected to be > 300 seconds  
• DeepSeek decision process:  

•  Retrieve historical solutions to similar events in the knowledge base  
•  Generate repair instructions (such as `STOP SLAVE; CHANGE MASTER TO...`)  
•  Automatic execution after triggering the pre-approval process through Jenkins  

• Safety mechanism: High-risk operations require manual secondary confirmation

• Data input: historical resource utilization + business growth forecast  

• DeepSeek prediction model:  

# Resource prediction prompt project prompt = """Based on the following server CPU usage time series data, predict the peak demand for the next quarter: Data format: [timestamp, value][...2024-07-01 12:00:00, 65%][...2024-07-01 13:00:00, 78%]... (8760 entries in total) Please output: { "peak_load": "Predicted value %", "suggested_instance_type": "AWS instance model" }"""

• Output results linked to Terraform automatic expansion

3. Data preparation and model training

• Collect historical work orders (more than 50,000), operation and maintenance manuals, and postmortem reports  
• Label entities: service name (Service), fault type (ErrorType), impact level (Severity)  

   # Use DeepSeek-7B base model python -m deepseek.finetune \ --model_name="deepseek-7b" \ --dataset="ops_dataset_v1.jsonl" \ --lora_rank=64 \ --per_device_train_batch_size=4

• Fault classification accuracy>92%  

• Command generation accuracy>85% (security audit required)

4. Security and Permission Design

1. Access Control

• Manage credential permissions for AI systems through Vault  
• Sensitive operations require OAuth2.0+RBAC approval  

• Automatically replace IP/hostname before training (e.g. 10.23.1.1 → <IP1>)  
• Use gRPC+ TLS1.3 to encrypt data transmission

1. Model hallucination risk

Through the above solutions, a step-by-step evolution from traditional operation and maintenance to intelligent operation and maintenance can be achieved. It is recommended to prioritize the implementation of log analysis and alarm aggregation modules, and significant efficiency improvements can be seen within 3 months.