6 LLMOps Best Practices
Large language models (LLMs) have captivated the interest and resources of business leaders from every industry. While mainstream LLMs are available for business use, enterprises stand to gain the greatest benefits by developing their own model or fine-tuning an existing one to serve their unique needs. This is where large language model operations (LLMOps) come in.
LLMOps is a type of machine learning operation (MLOps) that provides the tools, techniques, and infrastructure to develop, deploy, and manage LLMs. As this highly complex and multifaceted process is continually evolving, it is paramount that business leaders stay on top of current best practices.
This guide discusses LLMOps best practices that lead to smoother operations and more accurate and precise LLM outputs.
Data Preparation and Management
An LLM is nothing without data. Thus, LLMOps best practices emphasize sophisticated data
management, from preparation to protection and beyond. Some of the essential data preparation and management strategies include the following:
Regular transformation, cleaning, aggregation, deduplication, and data preprocessing are crucial to sustain the LLM's ability to leverage the data for tasks and output generation.
Checks must be performed to ensure the data is visible and shareable across data teams.
The data management software must be able to handle large volumes of data and ensure efficient data recovery throughout the LLM lifecycle.
The software must also have data versioning capabilities so that changes and development can be tracked over time and users can seamlessly transition between different dataset versions.
2. Data Security and Compliance
Data security and compliance are of utmost importance to LLMOps. The large volumes of data typically include sensitive and proprietary information that must be protected. Here are security and compliance strategies to implement:
Various encryption techniques must be used to protect data in transit.
Strict access controls must be established to determine who has access to what data.
The LLM must be continually scanned for vulnerabilities.
Security audits and tests must be conducted on the LLM regularly.
Compliance team must validate that operations adhere to all of the relevant regulations.
3. Model Management
In LLMOps, model management comprises the processes designed to optimize model performance. Here are some of the strategies that must be in place:
To manage the extensive volumes of data and complex parameters of LLMs efficiently, implement distributed training models that spread workloads across processors.
To keep the LLM updated and effective, regularly fine-tune the LLM with fresh data.
In terms of ethics, it is crucial to anticipate, discover, and correct biases in training data and outputs.
4. Deployment
When it comes to deployment, it is important to consider factors such as latency, cost, resource management, and security. Here is a closer look at each of these factors.
Latency: GPUs and TPUs are typically needed to optimize the inference speed of LLMOps. Specialized GPUs can enable faster operations.
Cost: Models require a massive amount of computational power and memory, both of which can be costly.
Resource Management: Hosting a model requires extensive resources, particularly when it comes to storage. Choose a deployment model, either cloud-based or on-premise that aligns with the key business requirements. Prioritize both optimization and scalability.
Security: As mentioned earlier, effective data security is crucial. Stay up to date with the evolving security needs and implement all of the required management, privacy, and security practices.
5. Continuous Monitoring and Maintenance
The work for LLMOps does not end at deployment. Instead, the work is ongoing. Advanced monitoring tools must be in place to detect any drifts in the model performance over time. Ongoing maintenance tasks include updates, bug fixes, and performance enhancements, largely through retraining and new data infusions.
6. Human Involvement
Human intervention is crucial to the success of LLMOps. For example, reinforcement learning from human feedback (RLHF), also referred to as RL from human preferences, can improve LLM training by fine-tuning a model with human prompts. End-user feedback can be critical to evaluating LLM performance, particularly for LLM tasks that are open-ended and subjective. Furthermore, it is beneficial for those involved with LLMOps to participate in the open-source community to stay on top of trends, updates, and best practices.
LLMOps with Encora
Encora's team of software engineers is experienced with implementing LLMOps and innovating at scale, which is why fast-growing tech companies partner with Encora to outsource product development and drive growth. We are deeply expert in the various disciplines, tools, and technologies that power the emerging economy, and this is one of the primary reasons that clients choose Encora over the many strategic alternatives that they have.
To get help implementing LLMOps, contact Encora today!