AI in Sales
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- REDIRECT AI in Sales Server Configuration
AI in Sales Server Configuration
This document details the server configuration required to effectively run and maintain an AI-powered sales platform. It is aimed at new system administrators and server engineers joining the team. This guide assumes a base understanding of Linux server administration and networking concepts. Please refer to the Server Administration Basics page for foundational knowledge.
Overview
The "AI in Sales" platform utilizes a microservices architecture, demanding a robust and scalable infrastructure. Core components include a data ingestion pipeline, a feature engineering service, several machine learning models (prediction, recommendation, and lead scoring), an API gateway, and a front-end user interface. Proper server configuration is critical for performance, reliability, and security. We'll cover the essential components and their recommended specifications. See Microservice Architecture Overview for a detailed diagram.
Hardware Requirements
The following table outlines the minimum and recommended hardware specifications for each server role. Note that these are *baseline* recommendations and may need to be adjusted based on data volume and user load. Consider utilizing Cloud Computing Resources for elasticity.
| Server Role | Minimum CPU | Minimum RAM | Minimum Storage | Recommended CPU | Recommended RAM | Recommended Storage |
|---|---|---|---|---|---|---|
| Data Ingestion | 4 Cores | 16 GB | 500 GB SSD | 8 Cores | 32 GB | 1 TB SSD |
| Feature Engineering | 8 Cores | 32 GB | 1 TB SSD | 16 Cores | 64 GB | 2 TB SSD |
| Prediction Model | 16 Cores | 64 GB | 500 GB SSD | 32 Cores | 128 GB | 1 TB SSD |
| Recommendation Engine | 8 Cores | 32 GB | 500 GB SSD | 16 Cores | 64 GB | 1 TB SSD |
| Lead Scoring Model | 8 Cores | 32 GB | 500 GB SSD | 16 Cores | 64 GB | 1 TB SSD |
| API Gateway | 4 Cores | 8 GB | 250 GB SSD | 8 Cores | 16 GB | 500 GB SSD |
Software Stack
We utilize a specific software stack to ensure compatibility and maintainability. Detailed installation instructions can be found on the Software Installation Guide page.
| Component | Version | Notes |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Security updates are crucial. See Security Hardening Guide |
| Database | PostgreSQL 14 | Used for storing sales data and model metadata. |
| Programming Languages | Python 3.9, Java 17 | Main languages for model development and API services. |
| Machine Learning Frameworks | TensorFlow 2.10, PyTorch 1.12 | Used for building and deploying AI models. |
| Containerization | Docker 20.10 | Used for packaging and deploying microservices. |
| Orchestration | Kubernetes 1.24 | Manages the deployment and scaling of containers. |
Network Configuration
The AI in Sales platform requires a well-configured network. All servers should be behind a firewall (see Firewall Configuration for details). Internal communication between microservices should be secured using TLS. The API Gateway is exposed to the internet and requires careful monitoring and security measures. Refer to the Network Topology Diagram for a visual representation.
| Network Element | IP Address Range | Purpose |
|---|---|---|
| Data Ingestion Servers | 192.168.1.100-110 | Ingest sales data from various sources. |
| Feature Engineering Servers | 192.168.1.120-130 | Process and transform data for model training. |
| Model Servers (Prediction, Recommendation, Lead Scoring) | 192.168.1.140-160 | Host and serve AI models. |
| API Gateway Servers | 192.168.1.200-210 | Provide a public API for accessing the platform. |
| Database Server | 192.168.1.50 | Store and manage data. |
Monitoring & Logging
Comprehensive monitoring and logging are essential for identifying and resolving issues. We utilize Prometheus and Grafana for monitoring and the ELK stack (Elasticsearch, Logstash, Kibana) for logging. Alerts should be configured for critical metrics such as CPU usage, memory usage, disk space, and API response time. See the Monitoring and Alerting Guide for detailed instructions. Regular log analysis is crucial for identifying security threats and performance bottlenecks. The Log Analysis Procedures document outlines best practices.
Security Considerations
Security is paramount. All servers should be regularly patched and hardened. Access control should be strictly enforced, and all communication should be encrypted. Regular security audits are recommended. Consult the Security Best Practices document for detailed guidance.
Data Backup and Recovery procedures should be in place to protect against data loss. Disaster Recovery Plan outlines the steps to take in the event of a major outage.
Contact Support if you encounter any issues. Glossary of Terms can help clarify unfamiliar concepts. Troubleshooting Common Issues provides solutions to frequently encountered problems.
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Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️