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LLMOps for Platform Teams: How LLMOps Powers the GenAI Revolution

Generative AI has risen to prominence as the next technology revolution. It’s driven by the surging adoption of Large Language Models (LLMs) such as GPT and Llama, machine learning models that are capable of understanding the meaning of written text to then produce new content.

LLMs have huge potential to transform industries so it’s no surprise that businesses are fast developing new solutions that use them. Yet it’s critical this experimentation occurs efficiently and within safe guardrails that safeguard data privacy, security, and compliance concerns. Large Language Model Operations (LLMOps) is a strategic approach to LLM delivery that enables enterprise platform teams to achieve these objectives.

In this post, we’ll introduce LLMOps and its importance to platform engineers. We’ll then analyze some of the challenges that LLMOps can present, before discussing best practices that facilitate the successful acceleration of gen AI adoption. This will allow you to successfully utilize LLMOps to develop large language models that add value to your organization.

What is Large Language Model Operations (LLMOps)?

LLMOps is an LLM-specific implementation of MLOps, itself an application of DevOps principles to machine learning. LLMOps aims to enable the continuous development, deployment, validation, and iteration of Gen AI applications based on large language models. It results in a more efficient delivery workflow that facilitates seamless communication between developers, operators, and data scientists.

LLMs are complex, computationally intensive, and potentially harmful if improperly trained and tested. For these reasons, it’s imperative that a robust process is used to create them. LLMOps provides the structure that permits safe adoption of LLMs, without compromising efficiency or your ability to innovate on novel AI solutions.

Some of the key components of LLMOps include:

  • Model training and fine-tuning: Providing a framework for reliably training and fine-tuning LLMs from their foundation data, such as adaptation of GPT to suit specific workloads.
  • Model serving and deployment: Deploying the completed LLM to production infrastructure and making it available to the apps that will use it.
  • Model monitoring and maintenance: Continual monitoring of the LLM and its performance to reveal optimization and improvement opportunities.
  • Governance and compliance: Enforcement of guardrails that ensure the LLM can be governed effectively throughout its life.

LLMOps enables improvements across these areas by establishing tools, workflows, and communication methods that promote continual iteration and experimentation. In much the same way as DevOps simplifies software delivery by bringing developers and operators closer together, LLMOps describes a holistic approach to LLM development that makes it more likely gen AI projects will succeed.

LLMOps Deployment and Management Challenges

LLMOps is not without problems. Although an effective LLM development platform will allow you to efficiently tune foundation models and evaluate results, there is more to successful gen AI adoption than the model itself. The deployment and management of “self hosted” LLMs is a unique challenge that platform teams must be prepared to address.

Foremost among the problems is the complexity inherent in deploying LLMs at scale. These models are computationally expensive and yet are often used in applications where low latency queries are essential—users don’t want to be waiting multiple seconds for an AI-powered app to respond.

To solve this issue, it’s vital that scalable infrastructure is used, permitting your LLM servicemodel to dynamically resize capacity as demand varies. Particular attention must also be paid to training requirements, where large memory and storage quotas are needed to ingest your datasets and test the LLM’s performance.

Dedicated AI platforms offer a compelling way to maximize LLM efficiency and reduce operational complexity. For example, Rafay makes it possible to deploy LLM workloads to any public or private cloud, from one consistent interface. This makes it easier to centrally manage provisioned resources and redistribute workloads as scaling requirements change.

Operation of LLMs also requires platform teams to navigate distinctive data privacy and security concerns. It’s necessary to implement training guardrails that mitigate the risk of sensitive information being leaked by the LLM, such as a user’s data being revealed to another individual during a response to a query.

Similarly, it’s vital to ensure the model and its infrastructure is robustly protected against more general threats; any security weakness that reveals queries or responses the model has handled could expose highly sensitive information that’s valuable to an attacker. Platform engineers need to liaise with ML security experts to ensure independently auditable safeguards are enforced.

Best Practices in LLMOps

LLMOps enables platform teams to more effectively manage the lifecycles of large language models. To achieve this, it’s important to follow best practices that are proven to contribute to optimal LLM delivery. The following techniques cover various aspects of LLM operation and monitoring.

1. Develop a Robust Model Serving Architecture

Models need to be served from robust infrastructure that’s capable of providing the performance your LLM app demands. This requires consideration of both compute resources and network bandwidth—low latency, high throughput connections are essential to ensure rapid LLM response times.

To successfully develop a resilient LLM hosting architecture, it’s important to evaluate different infrastructure options including public cloud, self-managed, and multi-cloud. Combining several different types of infrastructure in a hybrid cloud approach can also be an effective way to maximize cost effectiveness and make your deployments more robust. Try using a cloud management platform for LLMOps and MLOps with AI support to consistently configure your environments for serving models and launch new model releases.

2. Ensure Infrastructure Scalability

Powerful infrastructure scalability is critical to successful LLM operation. Because models are large, complex, and typically tasked with responding to user queries within seconds, you need to develop strategies that ensure resources can be dynamically scaled as load changes. Try containerizing your models and deploying them with an AI-centric Kubernetes management platform to enable responsive scaling changes that can be more conveniently controlled.

Dedicated AI/ML solutions are much more likely to offer the scalability LLMs require, as opposed to generic cloud hosting platforms and managed container orchestration services. Commercial options will provide specifically allocated resources including high-end CPUs and GPUs, alongside enhanced networking layers that facilitate the low-latency connections required for many LLM interactions.

3. Optimize Model Performance

Infrastructure and scalability improvements aren’t the only contributors to LLM performance: it’s also crucial to optimize the model itself, although this step is often overlooked in favor of simply deploying more instances with more powerful resources. Yet this results in costly waste and can mean users still end up waiting too long to receive responses to their gen AI requests.

LLM models utilize billions of parameters and are inherently compute- and memory-intensive as a result. However, inference performance can be increased by making use of more efficient memory allocation modes and tuning the model’s code to suit the hardware it’s deployed to. Likewise, when running in resource-constrained environments—such as on-device—improvements can be attained by dialing back the compute precision to achieve a better balance between the two main user outcomes of speed and output accuracy.

Quality is the second dimension of model performance. The text generated by the LLM needs to be relevant to the user’s input and free of inaccuracies and hallucinations. To improve the model’s output, it’s vital to train the model using representative data and conduct regular testing that’s based on real-world inputs. Utilize automated testing and performance analysis tools such as LLMPerf and LangSmith to debug models and find what’s causing unexpected results.

Governance and Compliance in LLMOps

Governance and compliance is an essential part of LLMOps. Your models need to maintain continual compliance with applicable data privacy regulations and ethical guidelines. These could originate both from outside your organization and as internal policies that you develop.

Good governance depends on your ability to implement detailed observability for your models and their operation. You need to understand the outputs the model is producing and how it arrives at them. If the model appears to be producing responses that fall outside its remit—such as a support chatbot that begins discussing unrelated sensitive topics—then you should be capable of detecting this and implementing safeguards that mitigate the risk.

Documentation is closely linked to compliance. Documenting how models have been implemented, what they’ve been trained on, and how they’re operated ensures this valuable knowledge is retained, ready for future developers and data scientists to consume. It also provides useful context during compliance audits by ensuring there’s a readily accessible design reference to benchmark the model’s actual performance against.

Monitoring, Logging, and Debugging for LLMOps

As we’ve touched on above, continuous monitoring and logging capabilities are an essential part of LLM operation. They allow you to effectively debug problems and maintain governance of your gen AI solutions.

Purpose-built LLM observability platforms make it easier to aggregate context from across different LLM endpoints. Solutions such as Datadog and Qwak provide tools for tracing LLM operations to determine why specific output was produced, allowing you to develop improvements to your models and their training data that result in accuracy enhancements.

Research papers have demonstrated that LLMs can be taught to self-debug, but these techniques remain novel. More common approaches to debugging revolve around regular model testing and interrogation. Platform teams should build evaluation datasets that can be used to compare an LLMs output to the expected result for a particular input.

The Rise of Enterprise Platform Teams in LLMOps

Behind all the topics we’ve discussed, platform teams hold responsibility for ensuring stable LLM development and operation. Although LLMs are often seen as the domain of ML experts and data scientists, platform engineers provide dedicated support by provisioning suitable infrastructure, enabling robust scalability, optimizing performance, and configuring effective observability.

Platform teams consolidate the efforts of other groups by engaging in close collaboration with data scientists and model developers. In implementing the LLMOps workflow that allows models to be efficiently built, validated, and deployed, the platform team has an integral role in fostering a culture of continuous gen AI improvement and innovation. They handle the more repeatable parts of the LLM workflow, allowing specialist teams to stay focused on actually building the models.

Investing in an LLMOps platform team is hence a strategy for maximizing your ROI on generative AI initiatives. Setting up a dedicated ML pipeline for model validation and deployment supports developers with the tools they need to efficiently launch new LLMs into production. This shortens the lead time and improves the quality of AI-powered applications.

What Next?

LLM-powered gen AI is one of the most in-demand technologies in today’s business environment. Platform teams play a critical role in successful LLM adoption by developing the tools and processes that allow models to be built, trained, deployed, and compliantly maintained. LLMOps provides the structure needed to achieve this by improving efficiency and enabling clear communication between different teams.

If you’re building new LLM solutions, then following best practices such as regularly tuning models and dynamically scaling infrastructure resources will give you a competitive advantage that lets you bring gen AI to market faster. You can read our whitepaper on how to accelerate AI and ML initiatives to learn more, or check out the Rafay AI Suite to begin centrally managing your LLM workloads across your public and private cloud environments.

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