Securing and Configuring AI Environments: AI in Operations

The artificial intelligence (AI) revolution is here. If you’ve been following the meteoric rise of companies like NVIDIA, you understand that the AI revolution isn’t just a passing hype cycle. 

To train complex models like Large Language Models (LLMs), organizations are turning back the dial to private data centers and high-performance computing (HPC) infrastructure — which requires thousands of servers all working in harmony. This massive scale presents huge challenges in terms of management, security, and compliance. 

How can you manage this enormous scale? You’ll need to start by treating thousands of servers as a single, colossal entity. If this sounds familiar, you’re right — this problem is best handled by desired state solutions like Puppet that provides local, on-server processing, low network utilization, and at least 48 state checks per day, per server.

The Current State of AI 

The Good

AI is here to help... right? No matter how you feel about AI, it’s undeniably a better way to handle common operational tasks and make your organization more efficient. Here are a few use case examples: 

Improved Efficiency 

• Automated Common Tasks: AI can handle repetitive tasks like incident ticket creation, software updates, and system monitoring, freeing up IT staff for more strategic work. 
• Increased Speed and Accuracy: AI can process vast amounts of data quickly and accurately, leading to faster problem resolution and improved decision-making. 
• Smarter Use of Resources: AI can optimize resource utilization by predicting workload fluctuations and adjusting resources accordingly. 

Proactive Problem Solving Predictive 

• Analytics: AI can analyze past data to find patterns and predict potential issues before they occur, cutting back on downtime. 
• Anomaly Detection: AI can detect unusual patterns in system behavior, indicating potential problems or security threats. 
• Root Cause Analysis: AI can quickly identify the underlying causes of IT issues, speeding up problem resolution. 

Better Decision Making 

• Data-Driven Insight: AI provides valuable insights from large datasets, helping IT teams make informed decisions. 
• Optimized IT Spend: AI can analyze IT costs and find places where you could save. 
• Improved Service Delivery: AI can help IT teams deliver better services by understanding customer needs and preferences. 

Stronger Security 

• Threat/Anomaly Detection: AI can identify and respond to cyber threats, unusual traffic, or abnormal user behavior in real-time, protecting sensitive data. 
• Incident Response: AI can automate routine security tasks, reducing response time to incidents. 

There are a lot of reasons to choose AI to save time, get more done, and to relieve your team resources. But the more parameters your AI model uses, the larger the cluster of servers is needed and therefore is to manage. If we apply something like Moore’s Law to LLMs, we will have a 1 Trillion parameter model in about 2 years. That is a ton of computing resources to manage and keep synchronized.

The Bad (and the Ugly)

AI offers huge advantages... but there is no such thing as a free lunch. Data quality, privacy concerns, and the need for skilled AI talent are just a few of the challenges that businesses face: 

Managing Data 

• Data Availability: You’ll need to consistently provide high-quality data to make sure that AI models can learn and make accurate predictions. 
• Data Consistency: Inconsistent, poor-quality data can affect AI performance. 
• Data Privacy: Handling sensitive IT data requires robust security measures to protect privacy. 

Development and Deployment 

• Complexity: Building and fine-tuning AI models can be complex and time-consuming — and then you’ll still need to find a way to integrate AI models into your existing IT infrastructure. 
• Talent Shortage: With the rapid rise of AI, finding skilled AI professionals has become a challenge across industries. 

Security and Reliability 

• Cybersecurity: AI systems can be targets for cyberattacks, requiring robust security measures. 
• Reliability: AI models may produce errors or unexpected results, impacting system reliability. 

Overall Value

• High Cost: Implementing, maintaining, and running AI solutions across thousands of servers can be expensive (no surprise there!).
• Measuring ROI: Calculating the overall cost benefit of AI can be challenging, making it difficult to justify investments.

New AI Environments, Old Techniques 

How do you manage consistent AI server infrastructure across on-prem, cloud, and high-performance computing deployment patterns while also staying ahead of cost, keeping everything synchronized, secure, compliant, and handling an enormous amount of data?

Enter Puppet's desired state language and compliance content subscription for both Puppet Enterprise customers and Open Source Puppet deployments — and some familiar tactics for old school computing patterns. 

Why Does an AI Cluster Need to Meet Regulatory Compliance Standards? 

Managing a single AI cluster with policy as code is like treating your servers like “cattle,” not “pets.” The things you used to do to make one server compliant and updated need to be applied to the entire cluster, where thousands of servers can act like one giant server —with the AI platform and its potentially regulated training data sitting on top of that same server.

Everything old is new again: using high density servers that accomplish the LLMs from a single place may feel like old school computing, and it is. It’s how you can ensure compliance, enforce consistency, and manage data. Why manage through a cloud service or a managed server provider? With a few servers in your own data center, you will be in a stronger position to manage everything at once. 

By treating your AI cluster as a single, manageable entity, you can leverage the power of infrastructure as code to: 

• Automate provisioning: Rapidly spin up and tear down clusters based on workload demands. 
• Ensure consistency: Apply standardized configurations across all servers. 
• Manage updates: Deploy patches and upgrades efficiently. 
• Monitor performance: Track resource utilization and find problems fast. 

It’s not fancy, glamorous, or as trendy as AI, but it works. For organizations that need to train their AI using sensitive data, using a cluster is a way to keep everything in-house and secure. 

How Puppet Supports AI in Operations

Puppet has supported server consistency and compliance since the beginning — making it a perfect fit in the new world of AI. 

With thousands of servers involved, ensuring uniformity in configurations, updates, and security patches is critical. Puppet treats servers as "cattle" rather than "pets," as mentioned earlier in this article; this allows changes to be applied uniformly and quickly across multiple servers. 

What about security and reliability? AI often handles sensitive data, making compliance with regulations like CIS benchmarks mandatory. Puppet can automate the process of checking and enforcing these standards across the entire cluster, automatically stay ahead of patching, and let you know if something is wrong or out of compliance. 

AI isn’t going away, and the heavy load it places on your servers and infrastructure requires a smarter solution. Puppet runs checks 48 times a day, 7 days a week, to make sure that what you apply to one server is enforced across every server. 

If you use AI, it’s time to try Puppet. Get a demo of what Puppet can do today: 

PUPPET FOR AI