How can you make NLP systems perform better and scale more easily?

Using pre-trained model is an best idea. It reduce our time to train an model + cost. There are many open source pre-trained models are available, which we can use as a base and then try to build our own required NLP system, which is way more easier as compare with building from scratch. Note: Always check the model information, some models are available for educational purpose only and can't be use for commercial purposes. Please double check it.

This is a gem for anyone navigating the complex world of NLP systems. It shines a spotlight on a game-changing strategy: the use of pre-trained models like BERT, GPT-3, and T5. By highlighting their versatility and efficiency, it underscores how these models can be a game-changer for NLP tasks. If you're seeking to elevate your NLP game, this read is a must.

In the realm of Natural Language Processing, data is the lifeblood of success. This article provides a strategic gem for those aiming to elevate the performance and scalability of their NLP systems. It underscores the paramount importance of data quality in achieving outstanding results. By meticulously curating, cleaning, and enriching your data, you not only enhance its relevance but also ensure consistency and diversity tailored to your specific task and domain. The article explores a plethora of techniques, from active learning and data filtering to data normalization and validation, offering a comprehensive toolkit for data optimization.

Considering the current landscape of AI, where powerful models are available quite easily, fine-tuning on the right data for your specific use case is essential to ensure a good performance. Reflection on what the business problem is, and how to create and curate a dataset capable of answering that problem is crucial and the step where the actual value is added by the scientist.

When it comes to optimizing your data, you can’t overlook the importance of going slow to go fast. While not the most glamorous task in the process, your effectiveness in optimizing the data will directly correlate to improving the overall outcome and increasing efficiency. Something else that every organization should be exploring when working with data is AI-generated synthetic data. Not only can it reduce the time spent in perfecting your data set, it can greatly reduce time spent in bureaucratic activity by greatly reducing privacy risks while maintaining the credibility of the data. As AI concerns continue to rise, being proactive with responsible AI best practices will help you avoid unnecessary cleanup down the road.

It’s important here to reconcile business metrics with machine learning ones, and to balance performance with cost. Having a simple baseline for the problem you are considering ( this can be the current state of things or even a simple model) helps understanding if it’s worthy to search for that 0.1 in f1-score or not.

Make sure to use MLOps best practices. You deployment pipeline should be deterministic and repeatable. You often need to leverage cloud platforms, which enable high scalability and sizing features. The deployment part needs to be robust and monitorable. If you cannot monitor it, you cannot improve it.

Testing plays a crucial role in enhancing the performance and scalability of NLP systems. It involves verifying the functionality and usability of models in various environments and scenarios. Utilizing testing techniques like unit testing, integration testing, and regression testing ensures that models work as intended and are free of bugs. Load testing, stress testing, and performance testing assess a model's ability to handle high volumes of data or requests. Testing helps maintain model quality and stability while identifying and addressing potential issues or errors.

Having a continuous training pipeline is the way to go here. It can either monitor your database to determine when the new volume of data is sufficient to trigger a new training or be scheduled at a fixed time interval. And this pipeline will be responsible for training, evaluating and pushing your new model into production.

Beyond fine-tuning an existing model, utilising RAG (Retrieval-Augmented Generation) or SWA (Sliding-Window Attention) are great way to improve the performance of NLP systems. With RAG, you can get away with fine-tuning and using a smaller LLM, thus decreasing both latency and costs. This is done by practically storing all the information that the system needs to "know" stored in a vector database, from where the LLM queries relevant information ad hoc. And SWA, utilised by the recent Mistral-7b model, allows NLP systems to handle much longer sequences at smaller cost, and we can expect more LLMs developed using this technique, or its seemingly superior child StreamingLLM - which appears to promise a latency reduction of 20x.