LLMOps 101 - Why Your Business Needs It and How to Get Started

What is LLMOps?

LLMOps refers to best practices that ensure optimal performance of LLM models in a live enterprise environment. It combines data and financial management, modeling, programming, regulatory workflows, and infrastructure upgrades, so companies can deploy, scale and maintain generative AI for sustained business value.

A recent survey of more than 50+ large companies found that 96% were already leveraging generative AI for more than 3 use cases – a remarkable uptick for a new technology that burst on the scene a couple of years ago.

However, much of the scaling and adoption is around internal use cases. More than a quarter of the respondents expressed concern that the challenges of compliance and performance were holding them back from confidently deploying LLM-based solutions for external-facing use cases.

A proven LLMOps framework can help eliminate these and other barriers to production, encouraging enterprises to deploy LLMs wherever they see maximum RoI, without worrying about robustness or reputational risk.

Sources: The remarkably rapid rollout of foundational AI Models at the Enterprise level: a Survey

How are LLMOps different from MLOPs?

Most companies use MLOps frameworks to develop, deploy, and maintain ML models. Since LLMs and ML models are modeling techniques, the frameworks share similarities. But, since LLMs are more evolved, LLMOps place greater focus on resource provisioning, ethics and compliance, and indefinite performance monitoring.

LLMs (such as GPT and BERT) are pattern-sensing models that work with diverse data to create new content. This is unlike ML models, which are deterministic mathematical models drawing from structured datasets to deliver calculable outcomes, such as predictions and classifications.

The non-deterministic nature and greater sophistication of LLMs leads to specific challenges to be tackled during operationalization.

Suppliers (From Raw Material Providers to Intermediate Goods)

• Data and infrastructure challenges of handling large volumes of more varied data rapidly
• Higher governance complexities
• Higher risk and greater business impact resulting from the technology’s greater autonomy.

This table illuminates the evolution of LLMOps from current MLOPs practices to tackle the unique challenges during productionization.

Aspect	MLOps	LLMOps
Scope	Focuses on building structured methods to prepare ML systems for real-world applications.	Tailors LLM applications for real-world use while addressing unique engineering, architecture, security, and operational challenges in productionizing LLM solutions.
Infrastructure as Code & Workspace Management	Leverages IaC tools for providing compute, storage, and networking resources to develop, train, and monitor models. Manages development, staging, and production workspaces in cloud, local, and hybrid environments.	Resource provisioning must also include upgrading GPU/CPU architecture to meet the computational demands of LLMs - with an additional focus on the security, privacy, and robustness of the more complex application layer for safe, efficient serving.
Development Layer	Establishes reliable and transparent mechanisms for data quality, lineage, and exploratory data analysis. Components like feature stores, experimentation tracking, versioning, and model registry accelerate the development of robust, future-proof models.	Experimentation and testing workspaces are more critical and may require evolution. Data preparation is more complex, including the setup of vector databases for unstructured data. Emphasizes best practices for pre-training, prompt engineering, and model fine-tuning, along with optimizing training costs and time through parallelization and federated learning.
Functional Testing and Model Handover	Involves change management and PR best practices, code review, and functional refactoring.	Requires careful attention to response quality, risk, and impact analysis. Balancing LLM task-based performance with application consumption strategy is crucial.
Pipeline and Orchestration	Involves modularization, pipeline setup, orchestration, automated testing, and retraining.	Focuses on app pipelines and orchestration tools along with third-party Foundation Model API consumption, testing, and serving.
CI/CD and Releasing	Configures CI/CD tools and conducts unit and integration testing following best practices.	Also implements prompt augmentation techniques (e.g., RAG) and utilizes new LLM releasing strategies like A/B testing, shadowing, and canary releases.
ML Observability and Security	Addresses model decay, data drift, feature importance shifts, and tracks serving availability and latency.	Tackles additional complexities such as agents and control flow engineering. Integrates security, privacy, and ML observability. (SecMLOps). Tracks new metrics for hallucination, perplexity, usability, and factuality.

How does LLMOps work?

LLMOps involve a few core elements to deliver future-proof, stakeholder-centric LLMs. These cover a deep understanding and execution of the LLM lifecycle, selection of best-fit tools and platforms, sound scaling strategies, continuous learning, financial optimization practices, and long-term performance monitoring.

Below, we provide insights into these elements.

The LLM lifecycle

The LLM lifecycle is a series of logical steps that guide decision-makers who want to use LLMS in their businesses. These steps include:

• Problem definition: Identify the problems the LLM can solve
• Model selection: Decide which foundational model you are going to work with. It could be open-source or proprietary. Developing your own rarely makes business sense
• Data preparation: Based on the problem to be solved and the model chosen, collect and clean the diverse data needed and ensure it is ready in terms of quality, accuracy and availability
• Model pre-training and fine-tuning: Refine the model by teaching it through large, diverse datasets to build desired capabilities during pre-training and then fine-tune it for task-specific capabilities with smaller, focussed datasets
• Model evaluation: Rigorously gauge how the model is performing on the speed, accuracy and compliance fronts for its assigned tasks
• Model deployment: Run the model in real-life scenarios
• Continuous model monitoring: Frameworks and automation to keep a close eye on how the model behaves in terms of performance and compliance and proactively address concerns
• Model realization: Ensure the desired business impact has been created.

These are the stages every model goes through to add value in business scenarios. However, the activities for each stage become far more complex as the number of use cases increases and the organization grows in LLM maturity.

This is where LLMOps comes in. Each model or group of models can go through multiple lifecycles of reuse and rebuilding through the years for multiple or evolving use cases.

It helps you ask questions such as:

• Will integrating an LLM improve the current outcomes and efficiency for a use case? Will the implementation deliver RoI?
• How do you develop the capabilities to deploy and scale LLMs?
• What are the frameworks to robustly operationalize model monitoring with security, performance and compliance in sight?

LLMOps will optimize cost, effort and infrastructure upgrades along this journey and ensure performance, security and compliance.

Tools and platforms for LLMOps

Technology plays a key role in helping teams collaborate and implement LLMOps. The current landscape hosts a range of solutions from leading providers, third parties and firms with a scientific bent. A business can choose a best-fit solution based on criteria that matter such as industry and market requirements, integration needs, and budgets.

For instance, CPG firms may need more real-time learning and scalability but pharma and BFSI companies may look for platforms that boost security and privacy. A mid-sized firm may prefer open-source options such as Hugging Face and MLFLow while larger companies are likely to seek a bouquet of cutting-edge tools.

Here is an overview of some LLMOps platforms and solutions from major cloud providers and third-party players.

Major cloud providers

Microsoft Azure

Azure AI Studio is a comprehensive platform for developing, deploying, and managing AI models. It seamlessly integrates with other Azure services and CI/CD pipelines to streamline workflows and automate deployments. It supports model versioning through Azure DevOps.

The platform also offers built-in monitoring tools to assess model performance and carry out model evaluation and monitoring. Azure provides compliance management and governance tools, including role-based access control (RBAC) and data privacy features, critical to preserving the security and integrity of the diverse sensitive data LLMs tap into.

Amazon Web Services (AWS)

Amazon Bedrock is a fully managed service that allows users to access leading foundation models through a single endpoint. This capability enables users to build and scale generative AI applications using foundation models from various providers.

It offers integrated model versioning and provides simplified deployment processes with auto-scaling capabilities, vital given the resource-intensive nature of LLMs. It comes with in-built tools for tracking model performance and usage metrics.

Google Cloud Platform (GCP)

Google Cloud Vertex AI is a fully managed, unified AI development platform for building, deploying, and scaling ML models. Its robust version control capabilities allow effective version management.

Its built-in CI/CD pipelines and integration with Google Kubernetes Engine (GKE) facilitate seamless deployment across environments. Its integrated monitoring capability tracks model performance and resource usage in any environment, so they remain optimized and compliant as they grow in complexity. The solution provides detailed audit logs and compliance checks for streamlined governance.

Third-party LLMOps platforms

Deepset AI

Deepset AI is a platform that integrates data with LLMs to build customized applications. It supports model versioning and experiment tracking. It facilitates easy deployment through APIs and SDKs, allowing customized integrations.

It facilitates continuous monitoring and evaluation of model outputs in real time. Its advanced governance mechanism includes capabilities for stringent data security and compliance.

Valohai

Valohai is an end-to-end MLOps automation platform, from data extraction to model deployment, including LLM management. It offers built-in version control for datasets to manage experiments and artifacts precisely.

Its advanced capabilities automate deployment across cloud environments, monitor model performance, and facilitate audit trails ensuring compliance for high-stakes deployment.

Comet

Comet is a model evaluation platform that helps build ML models for real-world apps by streamlining the entire machine learning lifecycle. The platform focuses on tracking experiments and keeping records of all model versions.

It supports multiple deployment automation strategies across environments. It provides insights into performance and metrics, making it ideal for large enterprise teams.

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It is an exciting moment with providers and partners coming up with new and innovative applications drawn from core science and mathematics to add value to business.

How LLMOps help efficiently scale LLM deployments

While the adoption rates for LLMs are quite high, companies are likely to be at different stages of maturity. Some may have deployed a few use cases while some might be on the path to integrating LLMs into AI agents to oversee entire processes. Here, we propose a four-step roadmap a company can follow as it grows on its LLM journey.

a) Create an LLMOps landing zone

In the initial days, enterprise teams can formulate best practices and leverage best-fit tools to streamline the deployment and monitoring of a few LLMs. This lays the foundation for a deep model repository and state-of-the-art capabilities in the space.

b) Create repeatable LLMOps processes

After building a repository of foundational models and acquiring capabilities, teams must now develop repeatable processes for:

• Chaining prompts to automate processes, registering and versioning applications, logging data inputs and outputs, and analyzing and filtering results
• Developing rating mechanisms to validate output quality and perform security and privacy checks.

c) Develop reliable LLMOps processes

With repeatable processes in place, teams will now move on to boosting the resilience of these processes so they can dependably deliver fine-tuned, personalized models. This involves:

• Managing data dependencies and implementing organizational templates for LLMs
• Storing and logging prompts and testing lineage
• Reusing models to address new problems
• Setting up scalable pipelines and automated processes for model deployment
• Standardizing serving processes such as API testing, model rating, and reliability checks
• Establishing tradeoff controls to balance performance with cost
• Monitoring and analyzing models to detect and mitigate risks.

d) Scale LLMOps capabilities

With reliable industrial processes established to streamline the operationalizing of the LLM lifecycle, the company has a robust foundation for scaling its LLMOps.

• A repository exists of fine-tuned LLMs
• Control flows, agents, and training pipelines have been automated
• Prompt engineering has been enhanced with vector databases and prompt augmentation
• Model outputs are robust, reliable, and trusted by users and are continually optimized with monitoring and automated retuning and build capabilities
• The organization’s LLM governance function:
• Establishing tradeoff controls to balance performance with cost
• • Uses codified AI principles to ensure models are free from hallucination and bias
  • Ensures data security and privacy
  • Maintains compliance with data regulations and emerging LLM regulations.

Companies can initiate this roadmap precisely from their current stage of maturity. The systematic approach will deliver cost and efficiency optimization as well as reliable governance on the LLM journey.

Continuous learning and model retraining

Large language models (LLMs) are trained on real-world data to respond instantly in daily situations. However, on-the-ground realities change, and the model’s self-learning abilities may not tap into these shifts adequately during use case interactions. Even when trained with new data, models may not perform as well as before—a trait known as 'catastrophic forgetting.'

When this drop in performance happens post deployments, it leads to model staleness, and eventually, model drift.

This makes a well-thought-out, streamlined LLMOps approach to retraining and continual learning imperative.

A recent, comprehensive study, Continual Learning for Large Language Models: A Survey, proposed a framework that unites takeaways from the research in the area. It delineates continual learning for LLMs into different stages:

• Continual Pre-training (CPT) involves self-supervised training by the LLMs to improve knowledge and adapt to new domains.
• Continual Instruction Tuning (CIT) stage fine-tunes LLMs on a stream of supervised instruction-following data, empowering LLMs to follow users’ instructions while transferring acquired knowledge for subsequent tasks.
• Continual Alignment (CA) is possibly the most critical and complex for this technology, which interacts with humans conversationally and creates new content. CA tries to continuously align LLMs with human values over time.

The figure below from the research illustrates how this training framework implemented iteratively helps the model evolve increasingly nuanced responses over time. Most modern platforms offer automated pipelines and workflows for continual retraining.

Sources: Continual Learning for Large Language Models: A Survey

LLM monitoring and performance management

There is considerable risk in deploying LLMs at scale. Hallucinations may pop up in sensitive interactions with external stakeholders. The data engines may deteriorate over a period driving down the output accuracy. The prompts may drift and no longer fetch the desired results.

LLM monitoring and performance management involve keeping a close eye on metrics that track or predict these mishaps, equipping teams to act preventively.

Choosing the right metrics to monitor is possibly the most important step in LLM monitoring. Consider using a matrix like the one below to prioritize your monitoring metrics:

Sources: BCG Executive Perspectives CEO's Roadmap on Generative AI

• While model performance such as latency and error rates should be monitored, metrics that measure dimensions unique but important to LLMs, such as input diversity, also need to be considered
• It also falls under the purview of model monitoring to continually evaluate the suitability of factors that comprise the model’s environment, such as cost, infrastructure usage, data quality, and user satisfaction.

Enterprises are equipping themselves with LLM monitoring solutions that automate their monitoring frameworks. The solutions provide up-to-date overviews of metrics and trends, alerts for quick remedial action, and analyses for long-term adaptation. However, companies must ensure that these solutions:

• Go beyond performance benchmarks like BLEU, ROUGE, and METEOR to identify unexpected intricacies around anomalies, security, and privacy specific to their use cases and business contexts
• Suggest or execute short-term and long-term prescriptive actions when issues arise
• Carry out these tasks efficiently at scale
• Possess the flexibility to add or remove features and capabilities as the models and monitoring framework evolve.

What is Responsible AI for LLMs?

Responsible AI refers to designing, deploying and scaling AI systems with the highest priority on ethical and legal compliance, user trust, and safety. The approach requires careful consideration of fairness, transparency, and security, so AI benefits the corporation and its larger ecosystem while minimizing risk.

More than any other technology, LLMs learn unsupervised from live data streams and constant conversations with humans. If this learning is not monitored, it can lead to:

• Non-compliance with internal and external guidelines on communication and privacy
• Responses known as “hallucinations,” which are sometimes merely out of context but at times can be unethical and even harmful.

The goal of responsible AI is to ensure from the get go that LLMs are set up to minimize these occurrences, preventing the erosion of user trust in these expensive implementations.

According to EY, there are three principles to embed trust into every facet of AI

• Purposeful design
• Agile governance
• Vigilant supervision.

Sources: How do you teach AI the value of trust? | EY - Global

To ensure responsible AI, here are some components LLMOps must cover:

• Fairness and Bias Mitigation: Throughout the lifecycle, from the data used, model selection and evaluation, and the deployment and ongoing monitoring, there should be a focus on ensuring that LLMs do not acquire prejudices and treat everyone equally.
  

  During the key deployment and operationalization stages and for indefinite monitoring, some of the metrics used are:

  • Toxicity Ratio: Higher toxicity ratio implies a more and severe presence of offensive phrases and words
  • Language Polarity: Difference in polarity of articulation across groups enables us to see how biased the model is across professions, ethnicities, etc.
  • Toxicity Ratio: Higher toxicity ratio implies a more and severe presence of offensive phrases and words
• Transparency and Explainability: As the technology becomes increasingly central, industry and academia are striving to do away with the “black box” analogy.
  

  However, the very aspect that makes LLMs superior - their rapid, unsupervised self-learning - also makes it much harder to explain their actions. But since implementations are racing ahead, industry practitioners are addressing the explainability component with metrics such as:

  • Confidence Score: Measures the confidence in the accuracy of the response
  • F1 Score: Measures accuracy as a function of precision and recall
  • Faithfulness: Measure of alignment with source material.

  Some of the effective techniques currently used to calculate these metrics include using other LLMs (as-a-judge) or deep learning models.

  The metrics we state here are selected as per the organization's priorities and needs. Up to twenty-two in number, they are ideally embedded into monitoring frameworks, to be evaluated constantly and trigger immediate corrective action when necessary. Wherever possible or required, the metrics are compared against ground truths. Additionally, guardrails are set up to establish safe, organization-specific intervals for each metric and these guardrails are regularly reviewed based on evolving information.

• Security and Compliance: Growing digitization has pushed cybersecurity centre stage. The mass deployment of LLMs—a landmark technology with human-like capabilities such as self-learning and the creation of new content—further increases the vulnerability of companies. Let us examine how this aspect is tackled by Responsible AI:
  

  1. Data Protection and Privacy: LLMs are trained on large volumes of data and continuously gather more as they interact with stakeholders. Mechanisms such as encryption should be employed to keep the data safe. Meanwhile, compliance with regulations such as GDPR, CCPA, and HIPAA should be ensured.

  Some metrics that are useful to gauge privacy are:

  • PII Violation: Measures the proportion of personally identifiable information
  • PHI Violation: Measures the presence of personal health information
  • Sensitivity Violation: Tracks the leakage of sensitive information such as IP
  • Privacy Loss: Estimated risk of privacy breaches

  These metrics can be used both for the input data as well as model outcomes at any stage.

  2. Protecting Models: With so many users interacting with the model, the attack surface expands dramatically. Robust monitoring should proactively identify harmful attacks such as prompt injection, and teams should be equipped with Standard Operating Procedures (SOPs) to respond immediately.

  As a part of LLMOps, regular security and compliance audits are effective in flagging issues in advance.

• Accountability: Any company deploying LLMs at scale should have a centralized team and key stakeholders in every user team who take on the role of responsible AI owners, providing critical human oversight.
  

  The Responsible AI team must work closely with industry bodies like the Responsible AI Institute to build a Responsible AI framework that is ahead of government and third-party requirements, and ensure that teams, processes and technology execute the framework faithfully.

LLM FinOps

Enterprises will likely have FinOps frameworks to optimize spending on their cloud initiatives.

However, LLMs, with their greater complexity on every front—from the data they use, model training, increased compliance needs, and continuous learning—require more resources and will necessitate multi-functional teams to create new financial estimation techniques. Let us look at some of the considerations these teams have to weigh:

1. Choosing the right provider: Given the large outlays involved, the first step is to decide which provider or partner to go with. Large companies have multiple providers and partners. The illustration below can help with these decisions:

2. Deciding on use cases and model complexity:When deciding which route to take, the teams also need to look at where in the company they want to integrate LLMs for maximum RoI.

BCG advises companies to quickly implement low-barrier use cases such as text summarization, while also identifying ‘golden’ use cases that will lend competitive advantage such as R&D for pharma firms. How powerful the LLMs need to be in terms of parameters and the levels of customization required is also a key cost consideration.

Both considerations require balancing trade-offs between the time and money invested and the likely returns. A matrix like the one below can help make business-aligned, financially sound decisions about the placement and power of LLMs:

3. Forecasting workloads: Once the bigger decision is made, new algorithms will have to be set up to forecast spending for the LLM cloud workloads. Your provider or partner is likely to have calculators that look at factors such as likely data volume growth, model retraining and compliance costs to support this forecasting.

LLMs are here to stay and are integrating into newer waves of technologies such as AI agents. Hence, enterprise leadership, technology teams, and business experts should come together to devise robust FinOps frameworks that combine iteration and rigour to find the right balance between investment and returns.

Reference: Cost Estimation of AI Workloads (finops.org)

Benefits of LLMOps

Enterprise Large Language Models (LLMs) are transforming business processes and boosting productivity, creativity, and collaboration. However, deploying and maintaining these sophisticated systems requires a methodical, multidimensional operationalization framework. This is where LLMOps comes into play.

The advantages of using LLMOps are significant.

• For large-scale LLM deployments, companies seek to maintain enhanced performance and reliability. With best practices and checkpoints for continuous monitoring and systematic retraining, LLMOps ensure models maintain peak performance indefinitely. This prevents model staleness and drift, maintaining critical stakeholder trust and a consistent experience.
• LLMOps also ensure scalability and efficiency of gen AI deployments. As organizations grow their AI initiatives, LLMOps provide the infrastructure and processes to scale the complex models effectively. Best practices proactively identify resource provisioning to act on, support data management, use case identification, and so on, while automation and workflows streamline testing, deployment, and model reuse across business problems. Additionally, LLMOps establish a common ground for multifunctional teams to accelerate initiatives.
  In the absence of these principles and pipelines, companies will end up with initiatives that break down while scaling, underperform due to inadequate provisioning, or have excess resources assigned.
• Responsible AI is the linchpin of successful gen AI deployments. LLMOps deliver on this with governance mechanisms across the lifecycle, to ensure AI initiatives adhere to internal and external regulatory requirements, maintain high ethical standards, and preserve user trust and safety.
  With transparency, security, and accountability in place, your ecosystem can wholly benefit from your generative AI with minimum harm and risk.
• Each step of the LLM lifecycle demands financial planning. Existing FnOps frameworks are a starting point, but teams should now devise new practices to decide how powerful the models should be, which uses cases to choose, and how much to invest in resources and training.
  Through careful tradeoffs, sophisticated workload forecasting, and informed decision-making, the overhauled FinOps component of LLMOps can ensure the sustainable and feasible RoI of these implementations.

In sum, setting up a robust LLMOps approach translates into lasting business value. Without LLMOps, organizations would not be able to confidently scale their initiatives across internal and external use cases, monitor safety and quality, and ensure RoI.

How Tredence can help

Who we are

• Tredence is a specialized AI Solutions company, driving value realization through last-mile adoption and impact.
• We are a Forrester and Databricks award winner with 94% CSAT.
• We fulfill the AI requirements of 8 out of 10 leading CPG companies and a similar proportion of global retailers.
• Since the advent of generative AI, we have successfully deployed 20 generative AI enterprise projects in the TMT, Industrial, CPG, and retail verticals.
• Our cross-functional team comprising data scientists, software engineers and business experts works closely with SMEs across your business to understand your requirements.
• We can unify elaborate tech stacks into a centralized platform architecture for LLMOps that addresses the complexities of LLMs effectively.

• Our accelerators across the lifecycle come together as as-a-service modules to drive down the delivery time in live business scenarios.

Our journey of building an LLMOps CoE for a global nutrition and pet care brand.

• Started with conducting 32 interviews with leaders and SMEs across the business.
• This helped Identify common challenges such as lack of Playbooks, standardized Ops Tools, process and framework and duplication of efforts due to silos.
• Based on technical evaluation through POCs, we helped shortlist 3 out of 20+ platforms.
• The emerging LLMOps CoE comprises:
  • A centralized platform architecture with pre-defined operation guidelines to eliminate siloes.
  • Reusable components, plug-and-play modules, and accelerators for cost and time optimization throughout the lifecycle.
  • SMEs for productionizing LLMs, training and upskilling programs and establishing ownership.
  • An MVP of the evaluation framework providing the ability to compare use cases/data across various LLMs using native cloud services.
  • A customizable CI/CD pipeline for promoting and deploying GenAI solutions. It provides seamless integration and automated workflows for efficient and reliable solution delivery.
  • We have developed an end-to-end monitoring and observability framework for GenAI as a microservice, enabling use case teams to plug-and-play monitoring without additional development effort
    • Azure and GCP cover basic content safety with only 5 metrics (hate, explicit and so on). Our service encompasses 22 metrics
    • Users are enabled with a unified GenAI monitoring console for model quality, content safety, tracing and system metrics
    • There is also a console that can be accessed by multiple teams to monitor LLM usage, performance quality metrics, and end-to-end logs traceability
    • An extensible Guard Rails framework helps ensure ethical standards, prevent biased or inappropriate outputs, and ongoing compliance with legal and regulatory requirements
    • Implementation of customized KPIs and metrics across 8 RAI pillars (Fairness, Explainability, Accountability, Security, Privacy, Safety, Data Integrity, and Reliability) in alignment with the RAI team. Includes Completed MVP featuring an RAI metrics/KPI dashboard.
  • Actively validating the environment over the last 2 months and helping to resolve platform issues.
  • Playbooks for tools and activities across the lifecycle.

We envision a 50% drop in LLM operationalization effort for the nutrition and pet care giant.

Conclusion

The proactive involvement of governments and international and industry bodies to regulate generative AI and recognize innovation in the space can be viewed as an acknowledgment that the technology is not just the new favourite on the block but will play a role in business and society for some time.

The rapid adoption rates across industries reflect this observation but business leaders are also sharing worries about reliability, practicality and RoI.

If your company wants to go beyond table stakes in the adoption and scaling of LLMs, reach out to your ecosystem to create an LLMOps framework that:

• Meets your unique business needs
• Optimizes cost and efficiency at scale
• Prioritizes Responsible AI.

Frequently Asked Questions