Because LLMOps falls within the scope of machine leaning operations, it might be overlooked or even referred to as “MLOps for LLMs,” but LLMOps should be considered separately as it is specifically focused on streamlining LLM development. Here are two ways that machine learning (ML) workflows and requirements specifically change with LLMs.

• Cost savings with hyperparameter tuning: In ML, hyperparameter tuning often focuses on improving accuracy or other metrics. For LLMs, tuning in addition becomes important for cutting the cost and computational power requirements of training and inference. This can be done by tweaking batch sizes. Since LLMs can start with a foundation model and then be fine-tuned with new data for domain-specific improvements, they can deliver higher performance for less.
  
  
• Performance metrics: ML models most often have clearly defined and easy-to-calculate performance metrics, including accuracy, AUC and F1 score. But when evaluating LLMs, a different set of standard benchmarks and scoring are needed, such as bilingual evaluation understudy (BLEU) and recall-oriented understudy for gisting evaluation (ROUGE). These require additional consideration during implementation.

LLMOps platforms, in addition, can provide what are thought of as typical MLOps functionalities:

• Data management
• Deployment process
• Model testing and training
• Monitoring and observability
• Security and compliance support
  

LLMOps can bring greater efficiency to a wide variety of tasks, including:

• Building vector databases to retrieve contextually relevant information.
  
  
• Continuous integration and delivery (CI/CD), where CI/CD pipelines automate the model development process and streamline testing and deployment. Tools like Jenkins GitLab CI/CD, and GitHub Actions help manage these pipelines, ensuring a smooth and efficient workflow. This enables seamless model updates and rollbacks, minimizing disruption to users. Versioning models and incorporating thorough testing practices can help catch issues early, ensuring that only well-performing models are deployed.
  
  
• Data collection, preparation and prompt engineering, drawn from a sufficient variety of sources, domains and languages.
  
  
• Data labeling and annotation with human input to provide complex, domain-specific judgment.
  
  
• Data storage, organization and versioning—with suitable database and storage solutions to aid data storage, retrieval and manipulation throughout the LLM lifecycle.
  
  
• Exploratory data analysis (EDA) to explore, prep and share specific data for the machine learning model lifecycle when you create editable and shareable data sets, tables and visualizations.
  
  
• Model fine-tuning for optimization of models for specific tasks or domains.
  
  
• Model inference and serving can manage the production specifics of testing and QA such as frequency of model refresh and inference request times. Enable your REST API model endpoints using GPU acceleration.
  
  
• Model review and governance to track model and pipeline versions and manage their complete lifecycle. This can enable collaboration across ML models using an open-source MLOps platform such as MLflow.
  
  
• Model monitoring, including human feedback to your LLM applications. Identify potential malicious attacks, model drift and identify potential areas for improvement.
  
  
• Prompt analytics, logging and testing.
  
  
• Prompt engineering, with tools to enable in-context learning rather than fine-tuning using sensitive data.
  

• Prompt execution to enable model optimization.
   
• Text creation and outputs (link resides ouside ibm.com) across a range of functions. LLMs can generate code, including scripting and automation for operating infrastructure. They create text, such as for documenting code or processes, and translate languages.

The primary benefits of LLMOps can be grouped under three major headings: efficiency, risk reduction and scalability.

Efficiency

LLMOps enables your teams to do more with less in a variety of ways, beginning with team collaboration. Efforts can be streamlined when data scientists, ML engineers, DevOps and stakeholders are able to collaborate more quickly on a unified platform for communication and insights sharing, model development and deployment—all resulting in faster delivery. 

Computational costs can be cut by optimizing model training, selecting suitable architectures and using techniques including model pruning and quantization. LLMOps can help ensure access to suitable hardware resources such as GPUs, for efficient fine-tuning, monitoring and optimizing resource usage. In addition, data management can be simplified when LLMOps promote robust data management practices, to help ensure high-quality datasets are sourced, cleaned and used for training.

Hyperparameters can be improved, including learning rates and batch sizes to deliver optimal performance, while integration with DataOps can facilitate a smooth data flow from ingestion to model deployment—and enable data-driven decision-making. 

Iteration and feedback loops can be accelerated by automating repetitive tasks and enabling fast experimentation. Using model management, LLMOps can streamline the start-to-finish processes of large language models, helping ensure the models are created, trained, evaluated and deployed optimally. 

Model performance can be improved using high-quality and domain-relevant training data. In addition, when constantly monitoring and updating models, LLMOps can ensure peak performance. Model and pipeline development can be accelerated to deliver higher-quality models and deploying LLMs to production faster.

Risk reduction

You can improve security and privacy by using advanced, enterprise-grade LLMOps to prioritize the protection of sensitive information, helping prevent vulnerabilities and unauthorized access. Transparency and faster responses to regulatory requests help ensure greater compliance with your organization’s or industry’s policies. 

Scalability

LLMOps enable easier scalability and management of data, which is crucial when thousands of models need to be overseen, controlled, managed and monitored for continuous integration, continuous delivery and continuous deployment. LLMOps can do this by improving model latency that can be optimized to provide a more responsive user experience.

Scalability can be simplified with model monitoring within a continuous integration, delivery and deployment environment. LLM pipelines can encourage collaboration, reduce conflicts and speed release cycles. The reproducibility of LLM pipelines can enable more tightly coupled collaboration across data teams, thereby reducing conflict with DevOps and IT, and accelerating release velocity. 

Workloads that can be managed smoothly, even as they fluctuate. LLMOps can handle large volumes of requests concurrently, which is particularly vital for enterprise applications.

For smoother operations, here are some suggestions to keep in mind.

• Community engagement: Engage with the open-source community to remain up-to-date with the latest advancements and best practices. Changes come swiftly.

• Computational resource management: LLM training involves extensive calculations on large datasets. Specialized GPUs can enable faster operations and accelerate data-parallel operations.

• Continuous model monitoring and maintenance: Monitoring tools can detect drift in model performance over time. Using real-world feedback on model outputs can refine and retrain the model.

• Data management: Choose suitable software to handle large data volumes, ensuring efficient data recovery across the LLM lifecycle. Track data changes and development with data versioning. Protect data with transit encryption and access controls. Automate data collection, cleaning and preprocessing to deliver a steady flow of high-quality data. Be sure that datasets are versioned in order to deliver seamless transitions between different dataset versions.

• Data prep and prompt engineering: Transform, aggregate and de-duplicate data on a regular basis. Make sure the data is visible and shareable across data teams.

• Deployment: To be most cost-effective, tailor a pre-trained model for specific tasks. Platforms including NVIDIA TensorRT and ONNX Runtime offer deep learning optimization tools.

• Disaster recovery and redundancy:  Backup models, data and configurations regularly in the event of disasters. With redundancy, you can handle system failures without impacting model availability.

• Ethical model development: Anticipate, discover and correct biases in training data and model outputs that can distort output.

• Human feedback: Reinforcement learning from human feedback (RLHF) can improve LLM training. Because LLM tasks are often open-ended, end-user feedback can be critical to evaluating LLM performance.
  
  

• LLM chains or pipelines: Facilitated by frameworks such as LangChain or LlamaIndex, you can link multiple LLM calls or external system interactions to enable complex tasks such as answering user questions.
  
  

• Model fine-tuning: Use open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow to improve model performance. Optimizing model latency is crucial for providing a responsive user experience. 
  
  

• Model monitoring:  Create tracking mechanisms for model and pipeline lineage, and versions to help ensure efficient lifecycle management of artifacts and transitions.

• Model training: Use distributed training to manage the huge scale of data and parameters in LLMs. Fine-tune models regularly with fresh data to keep them updated and effective. 
  
  

• Model security: Check the models often for vulnerabilities and conduct regular security audits and tests.

• Privacy and compliance: Validate that operations adhere to regulations such as GDPR and CCPA with regular compliance checks. With AI and LLMs in the news, there will be scrutiny.  

• Prompt engineering: Instruction-following models can follow complex prompts and instructions. Correctly setting these prompt templates will be critical to accurate and reliable responses, and reducing the chance of model hallucinations or prompt hacking.