Deploying Large Language Models (LLMs) is a step towards enhancing user experience. But, knowing where to start and which aspects to consider before LLM deployment is essential.

LLMs have been instrumental in powering everything from machine translation to content creation to virtual assistants and chatbots. Before deploying Llms, consider important factors, such as data, costs, memory management, evaluations, latency, and the right deployment strategy.

Considering these factors before deployment makes exploring of the LLMs in production easy and helps unlock its potential for building responsible AI-driven applications.

Eight Aspects to Consider Before Deploying LLMs

• Hardware and Infrastructure

As per a report by MLOps Community, “LLM Survey Report,”

• Of the 58 responses, 40 identified some infrastructure problems as a main challenge.
• 5% indicated their organization has built or integrated internal tooling to support LLMs.

Having robust hardware and infrastructure is essential before deploying an LLM. LLMs require powerful Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs) for training and inference.

They also need large storage capacities for storing Model weights, training data, and results. So, having fast and reliable storage systems is crucial before the deployment.

• Data

The efficiency and effectiveness of an LLM depend on the quality of the data used to train it. To ensure that an LLM performs its task optimally, it is essential to provide high-quality, relevant, and well-structured data for training before its deployment.

Pre-processing the data is crucial to eliminate any inaccuracies, biases, or noise within the dataset. Moreover, it is essential to curate the data carefully to ensure it is relevant to the task.

Investing time and effort in data pre-processing and cleaning can increase the LLM’s accuracy and reliability in generating results after deployment.

• Costs

MLOps Community’s report also states that the use of LLMs within an organization is still unclear due to the high costs and unknown ROI.

It may seem easy to measure the cost of using a general LLM like ChatGPT, but when using a custom system, there may be additional costs. These may be associated with staff and infrastructure needed to maintain and debug the system.

In some cases, hiring customer service reps rather than AI experts may be more cost-effective. It is also important to consider whether the LLM is cheaper and whether it will remain the same as the company and data grow.

• Maintenance

Most LLM systems are custom-trained in specific datasets. A downside to the neural networks LLM relies on is that they are challenging to debug.

With progressing technology, LLMs might develop the ability to “revise, “erase,” or “unlearn” false data that it has previously learned. So, before deploying an LLM, firms must set a process to update the LLM and eliminate poor responses regularly. One such process is fine-tuning. 

A best way to speed up the training process and save resources is to use pre-trained models as a starting point and then fine-tune them with task-specific data. This approach saves time and money and takes advantage of the existing language understanding in the pre-trained models.

Read more: How Large Language Model (LLM) Tuning Works

• Testing and Evaluation

An LLM does not require the user to anticipate every possible question variation to get an accurate answer. However, it’s important to note that “accurate” and “credible” aren’t necessarily the same.

Testing the most common questions and their permutations before deploying the LLM is a good idea. This will be helpful, especially if the LLM replaces a human or an existing machine process.

Also, the performance evaluation of LLMs is an ongoing challenge, as evaluation metrics are somewhat subjective. The standard metrics may not fully capture the intricacies of language understanding and generation.

Hence, it is essential to have an evaluation process in place that considers multiple perspectives of the LLM before its deployment. At the same time, employ human annotators to assess the LLM outputs and understand the quality of responses.

More importantly, establish clear evaluation criteria tailored to the task it performs, considering factors like context-awareness, coherence, and relevance.

• Memory Management

Memory efficiency is critical for maintaining low latency and ensuring a smooth user experience when serving an LLM in production. Memory usage optimization reduces response times and enables real time or near real time (NRT) interactions.

Also, memory management requires significant computational resources. So, before deploying an LLM, ensure that the teams know gradient checkpointing and memory optimization strategies to mitigate memory related challenges and successfully train LLMs.

• Latency 

Low latency is crucial in delivering a seamless user experience as users expect real-time or near-real-time responses, whether it’s a chatbot or a recommendation system. Hence, setting processes for optimizing latency is essential before deploying LLM.

As per MLOps Community’s report

• 5% of respondents highlighted the latency of models as an issue, as per
• 53% of respondents reported using Open AI’s API
• 6% cite the use of an open source model
• 3% use in-house models
• 4% use some other model provider’s API.
• Six respondents raised the question of whether it was better to be using a smaller, more task-specific model vs. calling an API

To achieve low latency, the choice of LLM API or hardware infrastructure input and output length, efficient memory usage, and optimized algorithms are some factors to consider.

Selecting the right LLM API and having hardware and distributed computing setup and methods like caching and batching will help achieve low latency and improve user experience.

• Data Privacy

Privacy concerns have become increasingly pronounced in the age of LLMs as the models have access to vast amounts of sensitive data. It is crucial to prioritize user privacy and implement appropriate measures to safeguard user data before deploying LLMs.

Employ data anonymization techniques, such as differential privacy or secure multi-party computation, to protect sensitive data. Additionally, have transparent data usage policies to build trust and respect user privacy rights.

Read more: What are Large Language Models (LLMs)

Conclusion

Deploying LLMs can significantly enhance the performance of the apps and services. However, it requires thorough planning, infrastructure, and steady maintenance.

Consider important factors before LLM deployment, like data quality, costs, maintenance, testing and evaluation, memory management, latency, and data privacy.

While this helps explore the evolving landscape of LLMs in production, it also helps build robust and responsible AI-driven applications.

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