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LLMOps: Turning AI Experiments into Business Outcomes

Shayde Christian
Cloudera

Production Standstill

This year, several data leaders began thinking about Large Language Model Operations (LLMOps) at a pivotal moment: when promising AI experimentation was ready to be transformed into business value. That's when the factory floor stopped. Quizzical practitioners and befuddled leaders debated questions they had foreseen but not answered — questions that could be summed up as:

How are we going to operationalize AI?

They would have benefited from an understanding of LLMOps during their experimentation phase — not because best practices and formal operational processes are most valuable during iterative exploration (some argue they are least valuable then) — but because LLMOps is the answer to many of the conundrums they faced:

How do I deploy AI apps widely?

Who does what?

How do I scale AI apps?

How do I monitor and control compute costs?

How do I maintain and improve model performance over time?

How do I reduce hallucinations and data privacy risks?

How do I improve response accuracy to drive business value?

The answer to all these questions? LLMOps.

Nuts and Bolts

Naturally formal operations processes fueled by best practices improve effectiveness, reliability, scalability, accountability and repeatability. They also reduce risk and improve efficiency. However, LLMOps' predecessors, MLOps and DevOps, offer no guidance on training and maintaining LLMs, optimizing model performance and accuracy, or hawkwatching a voracious kettle of GPUs.

New frameworks and workflows are needed to guide model building, training, and deployment. Without them, it will remain difficult to test model accuracy, ground hallucinations, and recalibrate drift. Even improvisational activities like exploratory data analysis benefit from LLMOps, as these processes preserve the history and impact of experimentation on model output.

For data leaders accountable for delivering value through AI, LLMOps is fundamental for monitoring and controlling compute costs and scaling enterprise AI applications. As AI scales, LLMOps automates pipelines and streamlines model development, testing, and deployment with continuous integration and delivery (CI/CD).

But the greatest advantage of LLMOps isn't technical — it's collaborative. Natural Language Processing (NLP) has lowered the technical barrier for non-technical users to extract high-value insights. With their deep subject-matter expertise, business users are becoming key contributors to AI workflows. The tool most essential for this collaboration? The simplest machine on the factory floor: the suggestion box.

The Suggestion Box

The most important tool in the LLMOps factory is the feedback loop — between prompter and responder, user and engineer, AI and AI. It's the secret to AI accuracy and effectiveness and the crux of LLMOps.

On the factory floor, users improve responses through better prompt engineering. This isn't technical engineering; it's simply about improving the plain-language instructions users submit to the AI. They give thumbs-up or thumbs-down responses and provide comments on model failure.

Behind the scenes, data analysts and data engineers, whether centralized in a Data and Analytics COE or distributed in a data mesh architecture, use feedback to improve the quantity or quality of data to increase response accuracy, or fine-tune the model to drive specific, desired behaviors.

The Beginning of the Assembly Line

Where should organizations start with LLMOps? A common construction pattern looks like this:

Model Selection: Organizations often target productivity and efficiency gains as drivers for AI deployment. They typically begin with a foundational model to democratize AI use across pockets of the enterprise. Model selection involves weighing quality, accuracy, functionality, speed, latency, and cost.

Model Adoption and Safe Usage: Foundational model deployment enables retrieval-augmented generation (RAG) to improve responses with internal data. Clear guidelines and guardrails must define which data users can expose to AI models, under what circumstances, and for which use cases.

Model Accuracy: This is the primary objective of LLMOps. Even minimal training in prompt engineering can significantly improve outputs and adoption. The suggestion box further boosts accuracy through iterative feedback.

Scalability: LLMOps determines how AI tools are deployed — whether by sharing prompts and tools across teams or by leveraging agentic frameworks where multiple specialized models collaborate on complex tasks.

Model Monitoring and Control: Use LLMOps to continuously monitor and improve model performance — accuracy, latency, safety, and compute costs.

Without LLMOps, you might find yourself operating in a sLLOMp. And while I'm not sure what that is, it certainly doesn't sound good.

Shayde Christian is Chief Data and Analytics Officer at Cloudera

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LLMOps: Turning AI Experiments into Business Outcomes

Shayde Christian
Cloudera

Production Standstill

This year, several data leaders began thinking about Large Language Model Operations (LLMOps) at a pivotal moment: when promising AI experimentation was ready to be transformed into business value. That's when the factory floor stopped. Quizzical practitioners and befuddled leaders debated questions they had foreseen but not answered — questions that could be summed up as:

How are we going to operationalize AI?

They would have benefited from an understanding of LLMOps during their experimentation phase — not because best practices and formal operational processes are most valuable during iterative exploration (some argue they are least valuable then) — but because LLMOps is the answer to many of the conundrums they faced:

How do I deploy AI apps widely?

Who does what?

How do I scale AI apps?

How do I monitor and control compute costs?

How do I maintain and improve model performance over time?

How do I reduce hallucinations and data privacy risks?

How do I improve response accuracy to drive business value?

The answer to all these questions? LLMOps.

Nuts and Bolts

Naturally formal operations processes fueled by best practices improve effectiveness, reliability, scalability, accountability and repeatability. They also reduce risk and improve efficiency. However, LLMOps' predecessors, MLOps and DevOps, offer no guidance on training and maintaining LLMs, optimizing model performance and accuracy, or hawkwatching a voracious kettle of GPUs.

New frameworks and workflows are needed to guide model building, training, and deployment. Without them, it will remain difficult to test model accuracy, ground hallucinations, and recalibrate drift. Even improvisational activities like exploratory data analysis benefit from LLMOps, as these processes preserve the history and impact of experimentation on model output.

For data leaders accountable for delivering value through AI, LLMOps is fundamental for monitoring and controlling compute costs and scaling enterprise AI applications. As AI scales, LLMOps automates pipelines and streamlines model development, testing, and deployment with continuous integration and delivery (CI/CD).

But the greatest advantage of LLMOps isn't technical — it's collaborative. Natural Language Processing (NLP) has lowered the technical barrier for non-technical users to extract high-value insights. With their deep subject-matter expertise, business users are becoming key contributors to AI workflows. The tool most essential for this collaboration? The simplest machine on the factory floor: the suggestion box.

The Suggestion Box

The most important tool in the LLMOps factory is the feedback loop — between prompter and responder, user and engineer, AI and AI. It's the secret to AI accuracy and effectiveness and the crux of LLMOps.

On the factory floor, users improve responses through better prompt engineering. This isn't technical engineering; it's simply about improving the plain-language instructions users submit to the AI. They give thumbs-up or thumbs-down responses and provide comments on model failure.

Behind the scenes, data analysts and data engineers, whether centralized in a Data and Analytics COE or distributed in a data mesh architecture, use feedback to improve the quantity or quality of data to increase response accuracy, or fine-tune the model to drive specific, desired behaviors.

The Beginning of the Assembly Line

Where should organizations start with LLMOps? A common construction pattern looks like this:

Model Selection: Organizations often target productivity and efficiency gains as drivers for AI deployment. They typically begin with a foundational model to democratize AI use across pockets of the enterprise. Model selection involves weighing quality, accuracy, functionality, speed, latency, and cost.

Model Adoption and Safe Usage: Foundational model deployment enables retrieval-augmented generation (RAG) to improve responses with internal data. Clear guidelines and guardrails must define which data users can expose to AI models, under what circumstances, and for which use cases.

Model Accuracy: This is the primary objective of LLMOps. Even minimal training in prompt engineering can significantly improve outputs and adoption. The suggestion box further boosts accuracy through iterative feedback.

Scalability: LLMOps determines how AI tools are deployed — whether by sharing prompts and tools across teams or by leveraging agentic frameworks where multiple specialized models collaborate on complex tasks.

Model Monitoring and Control: Use LLMOps to continuously monitor and improve model performance — accuracy, latency, safety, and compute costs.

Without LLMOps, you might find yourself operating in a sLLOMp. And while I'm not sure what that is, it certainly doesn't sound good.

Shayde Christian is Chief Data and Analytics Officer at Cloudera

Hot Topics

The Latest

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...