Large Language Models (LLMs) are increasingly vital in many business applications. As the use of LLMs expands, concerns about the complexity of implementation and management rise to the forefront of conversations surrounding the adoption of generative AI solutions. This is where Large Language Model Operations (LLMOps) come into the picture. LLMOps is a comprehensive set of technologies, processes, and strategies used to manage LLMs. There are five critical stages in the LLMOps lifecycle: development, training, deployment, monitoring, and maintenance. Each stage plays a vital role in efficiently and effectively operating LLMs.
This guide explores each stage of the LLMOps lifecycle. Let's begin.
1. Development
The LLMOps lifecycle begins with model development. The LLM foundation model is selected, configured, and prepared for a specific application in this stage. While it is possible to create a foundation model from scratch, this is rarely done as the process demands substantial resources. Those who opt to modify an existing model can choose between proprietary and open-source models.
Proprietary models are closed-source foundation models that are typically ready to use out-of-the-box. They are often preferred for their size and high performance, but the downside is that they can be costly and rigid due to their closed-source structure.
On the other hand, open-source models are cost-effective and flexible models available through community hubs like HuggingFace. The only drawback to open-source models is that they tend to be smaller and more limited in scope than proprietary models.
Once the foundation model is chosen, it is time to collect, curate, and preprocess the data that will be used to train the model. The data must be unbiased and representative of the desired content.
2. Training
The next stage is LLMOps training, an iterative process used to create and improve the LLM. Multiple rounds of training, evaluation, and adjustments are required to reach and sustain high levels of accuracy and efficiency. A variety of approaches can be used to adapt the LLM, and they include:
- Prompt Engineering - Prompt engineering is an iterative process of structuring inputs to get the desired outputs from the model.
- Fine-Tuning - Fine tuning is the process of retaining an existing model for a new, similar task using domain-specific data sets.
- Embedding - Embedding is the process of extracting information from the LLM to create vectors. This process makes it easier to automate repetitive search queries.
- External Data - Supplying the model with external data through connections with agents and vectors helps prevent hallucinations or outdated and erroneous outputs.
3. Deployment
When it comes time to deploy the LLM, LLMOps can do so through on-premise, cloud-based, or hybrid solutions. The choice between deployment methods largely hinges on infrastructure considerations such as hardware, software, and networks, as well as the organization's specific needs. At this stage, security and access controls are paramount to protect the LLM and its data from misuse, unauthorized access, and other security threats.
4. Monitoring
Once the model is available for use, its performance must be tracked across many tasks and domains. The model must be continually evaluated for accuracy and biases. This can be accomplished through automated tools, metrics, logs, and alerts that track the LLM while it is in use, ensuring it continues to deliver value with minimal issues.
5. Maintenance
Model maintenance is the next stage in the LLMOps lifecycle. Like monitoring, maintenance is an ongoing process that involves fixing bugs, updating the LLM with new data, and improving performance. Given the complexity of LLMs, changes must be tracked. This is where version control practices step in, allowing developers to conduct maintenance without permanently deleting or changing the existing model. Versioning allows for rollbacks if issues arise and ensures the reproducibility of effective improvements.
Aside from foundation model selection, all of the stages described can and should be repeated throughout the LLMOps lifecycle. Achieving success with the LLMOps lifecycle helps optimize resource usage, mitigate errors, and maximize the value of LLMs.
Optimize the Stages of the LLMOps Lifecycle with Encora
Encora has a long history of delivering exceptional software engineering & product engineering services across a range of tech-enabled industries. 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 optimizing the stages LLMOps, contact Encora today!
