Large language models (LLMs) are powerful Al algorithms with the ability to comprehend language and generate human-like responses. Because there are unique challenges associated with training and deploying LLMs, the more general approach of machine learning operations (MLOps) is insufficient. Large language model operations (LLMOps) fills in the gaps to help teams build high-quality models that deliver business value.
What is LLMOps?
LLMOps is a set of standardized processes, practices and tools for managing LLM development, deployment and maintenance. LLMOps typically involves six stages: training, evaluation, fine-tuning, deployment, monitoring and maintenance. Although the methodology for operationalizing an LLM is similar across industries, it is easily customized to fit the requirements of each use case.
Why it's needed
LLMOps provides structure and integrity for the development process. With a standardized framework, teams can develop high-quality models and pipelines with greater speed, efficiency, and reproducibility. LLMOps clearly defines roles and responsibilities and facilitates collaboration between teams. As the number and scale of LLMs increase, LLMOps allows organizations to effectively manage numerous models. With its strong emphasis on standardization, LLMOps reduces the risks that LLMs will run afoul of organizational policies or industry guidelines.
LLMOps vs. MLOps
The process of training LLMs differs from the one used to train ML models in a number of ways. Here are three examples.
Resource consumption: Training and fine-tuning LLMs is a compute-intensive process, often requiring the use of specialized GPUs designed for training and deploying LLMs.
Foundational models: Unlike ML models, most LLMs aren’t built from scratch. Instead, they’re developed using a pretrained foundational model that’s then refined with smaller, specific datasets that enhance its performance in a specific task or domain.
Measuring performance: Assessing the performance of an ML model is a straightforward process with clearly defined metrics for measurement, such as precision, accuracy and recall. Evaluating LLMs is more complex, involving evaluating factors such as language fluency, contextual understanding and factual accuracy.
5 core principles of LLMOps
Best practices are applied at each stage of the LLMOps process. Although this list is far from exhaustive, it provides an overview of how the principles are applied at each stage of the process.
Data management
Data is the primary ingredient for training and deploying LLMs, so it’s crucial to ensure it is carefully managed and well understood. LLMOps often begins with exploratory data analysis (EDA), the process of collecting, cleaning and exploring the data that will be used for model training and fine-tuning. Data scientists use EDA to gain important insights into the underlying patterns, relationships and distributions within the data. During this process, the datasets are analyzed to summarize their main characteristics, often with the help of data visualization methods.
Understanding the changes made to the data over the LLMOps life cycle is important for compliance, auditing and debugging purposes. Data versioning is the process of storing, tracking and managing the changes within a dataset. Maintaining a running record of how the data has changed over time is useful in multiple events in the LLMOps process, including data preprocessing, feature engineering and updates to a dataset.
Data security
LLMs are frequently trained and fine-tuned using sensitive data, such as personally identifiable information (PII). Implementing data security safeguards such as data encryption and role-based access controls helps protect sensitive data throughout the LLMOps process. In addition when sensitive data is included in the training dataset, stringent data anonymization and redaction techniques must be in place during model training to ensure sensitive data does not appear in the generated content.
Model management
The LLMOps process enhances model performance through efficient model training, evaluation and management practices. Model management begins with selecting a pretrained foundation model that meets the organization’s performance, size and compatibility criteria. From there the model is fine-tuned, using smaller datasets to refine its capabilities and improve performance for specific use cases.
Since LLMOps is an iterative process, maintaining robust model review and governance practices—including model versioning—helps teams track dependencies that may impact model performance. Model versioning makes it easier to test multiple models in various pipelines, tune model weights, track how models change over time, and ensure model accuracy and reproducibility.
Model deployment
LLMs can be deployed on either cloud-based or on-premises servers. Although either may be an option for certain use cases, cloud-based solutions provide benefits that are difficult to achieve with on-premise servers. Their rapidly scalable, on-demand data storage and compute resources help LLMOps teams optimize LLM production and deployment.
Monitoring and improving model performance over time
Establishing effective monitoring practices allows LLMOps teams to proactively detect and prevent issues and assess if the model is performing as intended, including with regard to aspects of responsible AI. Commonly used LLM monitoring and performance metrics include prompt and response quality, relevance, sentiment, and security. Model and data monitoring pipelines can be set up to alert LLMOps teams when models require intervention or further refinement.
Run your LLMs directly in Snowflake
The Snowflake Data Cloud plays an essential role in helping LLMOps teams manage the life cycle of their LLMs. With new features for streamlining development, deployment and maintenance processes, Snowflake provides the tools required to build and deploy these powerful applications.
Accelerate custom LLM development
With Snowpark Container Services, LLMOps teams can run their Docker containers inside Snowflake, including those that are accelerated with NVIDIA GPUs. NVIDIA’s pre-built and free Docker containers support a variety of AI workloads, including those that use the NeMo framework for LLMs. Snowpark Container Services allows teams to run workloads securely, directly inside their Snowflake account on their Snowflake data. Since the container is running inside Snowflake, organizations can provide users with user-defined functions (UDFs) that can be called from SQL to run advanced processing inside the container without operational burden. The flexibility provided by Snowpark Container Services is near-limitless and can accommodate open-source development tools like Jupyter notebooks, which provide a convenient way to experiment with and perform LLM fine-tuning.
Fine-tuning LLMs using unstructured data
Documents, emails, web pages and images are increasingly valuable data sources. But without an easy way to aggregate unstructured data and perform analysis on it, deriving valuable insights from it remains a challenge. Snowflake’s acquisition of Applica is changing that. This purpose-built, multi-modal LLM for document intelligence makes unstructured data more functional, minimizing the amount of manual document labeling and annotation required to fine-tune models specific to their documents. Applica is just one example of many leading LLMs that customers can run inside Snowflake in a secure and compliant way.
Pretrain and fine-tune your LLMs on Snowflake
Pretrained LLM models are fully functional out of the box, with model training data already assembled and model training complete. However, these generalist models require pretraining and fine-tuning to work well in specific use cases. Using your framework of choice, including the NVIDIA NeMo framework, available with the NVIDIA AI Enterprise software platform, this fine-tuning can be done directly on Snowflake.
Securely source fine-tuning data from within Snowflake
With Snowflake, LLMOps teams can source fine-tuning data from within Snowflake, so model training data is protected. Since fine-tuning happens directly on GPU nodes running on containers within a Snowflake account, confidential training data never leaves the account. In addition, the resulting model—now based on learnings from an organization’s confidential information—stays inside Snowflake in a secure Snowflake stage. It can also be deployed to perform contextual inference using Snowpark Container Services.
With Snowflake, LLMOps teams can develop and deploy powerful LLM-enabled applications. The Snowflake Data Cloud’s scalability, flexibility and performance help organizations unlock the capabilities of large language models.