In a rapidly evolving IT environment, MLOps (short for Machine Learning Operations) have become the secret weapon of organizations aiming to transform complex data into powerful, actionable insights. MLOps is a set of practices designed to simplify the machine learning (ML) lifecycle, helping data scientists, IT teams, business stakeholders, and domain experts collaborate to build, deploy, and manage ML models consistently and reliably. It emerged to address ML-specific challenges, such as ensuring data quality and avoiding bias, and has become a standard approach to managing ML models across business functions.
However, with the rise of large language models (LLMs), new challenges have emerged. The LL.M. requires technologies such as powerful computing power, advanced infrastructure, and rapid engineering to operate effectively. These complexities led to the specialized development of MLOps, called LLMOps (Large Scale Language Model Operations).
LLMOps focuses on optimizing the life cycle of LLM, from training and fine-tuning to deploying, scaling, monitoring and maintaining the model. It is designed to meet the specific needs of LL.M.s while ensuring they operate effectively in a production environment. This includes managing high computational costs, scaling infrastructure to support large models, and streamlining tasks such as rapid engineering and fine-tuning.
With the shift to LLMOps, business and IT leaders must understand the key benefits of LLMOps and determine which process is best to use and when.
Key Benefits of LLMOps
LLMOps builds on MLOps, providing enhanced capabilities in several key areas. The three main ways LLMOps can bring greater benefits to your business are:
- The democratization of artificial intelligence – LLMOps makes it easier for non-technical stakeholders to develop and deploy LLM. In a traditional machine learning workflow, data scientists are primarily responsible for model building, while engineers focus on pipelines and operations. LLMOps changes this paradigm by leveraging open source models, proprietary services, and low-code/no-code tools. These tools simplify model building and training, allowing business teams, product managers, and engineers to collaborate more effectively. Non-technical users can now experiment and deploy the LLM using an intuitive interface, reducing technical barriers to AI adoption.
- Faster model deployment: LLMOps simplifies LLM integration with business applications, enabling teams to deploy AI-driven solutions faster and adapt to changing market needs. For example, with LLMOps, businesses can quickly adjust models to reflect customer feedback or regulatory updates without the need for extensive redevelopment cycles. This agility ensures organizations can stay ahead of market trends and maintain a competitive advantage.
- The emergence of RAG – Many corporate use cases for the LL.M. involve retrieving relevant data from external sources, rather than relying solely on pre-trained models. LLMOps introduces the Retrieval Augmented Generation (RAG) pipeline, which combines a retrieval model with an LLM that sorts and summarizes information to obtain data from the knowledge base. This approach reduces hallucinations and provides a cost-effective way to leverage enterprise data. Unlike traditional ML workflows where model training is the primary focus, LLMOps shifts the focus to building and managing RAG pipelines as a core functionality in the development lifecycle.
Understand the importance of LLMOps use cases
With the general benefits of LLMOps, including the democratization of AI tools across the enterprise, it’s important to look at the specific use cases where LLMOps can be introduced to help business leaders and IT teams better leverage LLM:
- Safe deployment of models– Many companies start LLM development with internal use cases, including automated customer support bots or code generation and reviews, to gain confidence in LLM performance before scaling to customer-facing applications. The LLMOps framework helps teams simplify staged deployments of these use cases by 1) automating deployment pipelines that isolate on-premises environments from customer-facing environments and 2) enabling controlled testing and monitoring in a sandbox environment to identify and resolve failure modes; 3) support version control and rollback capabilities so teams can iterate on-premises deployments before deploying externally.
- Model risk management – The LLM itself brings more attention to model risk management, which has been a key focus of MLOps. Transparency of the data on which LL.M.s are trained is often murky, raising concerns about privacy, copyright and bias. Data hallucination has always been a huge pain point in model development. However, LLMOps solves this challenge. LLMOps enables real-time monitoring of model behavior, allowing teams to 1) detect and record hallucinations using predefined shortcuts, 2) implement feedback loops to continuously improve the model by updating prompts or retraining with corrected output, and 3) leverage more Good understanding and resolution of indicators that generate unpredictability.
- Evaluate and monitor model– Evaluating and monitoring stand-alone LLMs is more complex than traditional stand-alone machine learning models. Unlike traditional models, LLM applications are typically case-specific and require input from subject matter experts for effective evaluation. To address this complexity, automated assessment frameworks were developed in which one LL.M. is used to assess another LL.M. These frameworks create continuous evaluation pipelines that include automated tests or benchmarks managed by the LLMOps system. This approach tracks model performance, flags anomalies, and improves evaluation criteria, simplifying the process of assessing the quality and reliability of generated output.
LLMOps provides the operational backbone to manage the added complexity of LLM that MLOps itself cannot manage. LLMOps ensures organizations can address pain points such as the unpredictability of generated outputs and the emergence of new assessment frameworks, while enabling safe and effective deployments. Therefore, enterprises must understand the transition from MLOps to LLMOps in order to address the unique challenges of LLM within their organization and implement the right operations to ensure the success of their AI projects.
Looking ahead: Embracing AgentOps
Now that we’ve taken a deep dive into LLMOps, it’s important to consider the future of operational frameworks as AI continues to innovate. Currently at the forefront of artificial intelligence are agent AI, which are fully automated programs with sophisticated reasoning capabilities and memory that use LL.M.s to solve problems, create their own plans and execute that plan. Deloitte predicts that by 2025, 25% of enterprises using generative artificial intelligence will deploy artificial intelligence agents, and this proportion will increase to 50% by 2027. This data points to a future shift toward agent-based AI that has already begun, with many organizations already beginning to implement and develop this technology.
Therefore, AgentOps is the next wave of AI operations that enterprises should prepare for.
The AgentOps framework combines elements of artificial intelligence, automation, and operations to improve how teams manage and scale business processes. It focuses on leveraging intelligent agents to enhance operational workflows, provide real-time insights, and support decision-making across industries. Implementing an AgentOps framework can significantly enhance the consistency of AI agent behavior and response to abnormal situations, aiming to minimize downtime and failures. This will become necessary as more organizations begin to deploy and use AI agents in their workflows.
AgentOps is an essential component for managing next-generation artificial intelligence systems. Organizations must focus on ensuring observability, traceability, and enhanced monitoring of systems to develop innovative and proactive AI agents. As automation advances and AI responsibilities grow, effective integration of AgentOps is critical for organizations to maintain trust in AI and scale complex specialized operations.
However, before enterprises can start using AgentOps, they must have a clear understanding of LLMOps (as mentioned above) and how the two operations work together. Without proper education around LLMOps, enterprises will not be able to effectively build on their existing frameworks when implementing AgentOps.
