If you are curious on how to create an AI model what goes into building large language models (LLM) and fine tuning them for certain tasks and roles. This quick overview guide will provide a little detail on where you should start and the processes step-by-step you need to take to create an AI model for a wide variety of different applications. IBM has created a useful video going through the five steps of creating a new AI model offering further insight into how they are created.
If this is a new area of learning for you you will be pleased to know that the road to creating an effective AI model has never been more streamlined, thanks to advances in both deep learning and foundation models. These avenues offer unique advantages, whether you’re looking to build specialized applications like customer service chatbots or more universal solutions.
How to build an AI model
If you’ve ever wondered how to initiate this creative process, model creation can be segmented into five distinct yet interconnected stages: data preparation, model training, model validation, model tuning, and finally, model deployment. Each stage plays a pivotal role in ensuring your AI model is not only functional but also efficient and reliable.
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1. Data preparation
Data is akin to the lifeblood of AI. Without it, even the most advanced algorithms are rendered useless. Data preparation often involves dealing with enormous volumes—sometimes even petabytes—of data sourced from a multitude of domains. Here, data undergoes several processing steps, including categorization, filtering, and the removal of duplicates. In this stage, you might find it beneficial to establish connections with data repositories, akin to how IBM’s Watsonx.data functions in its AI workflow.
2. Model training
Once the data is prepped and primed, the next step is to train your model. This is where you’ll choose a foundational model that aligns with your project’s requirements. The selected data is then tokenized, acting as the building blocks for model training. You may be surprised to learn that this process can be computationally expensive and time-consuming. Hence, aligning your data and foundational model is crucial for efficient training.
3. Model validation
After the training wheels come off, it’s time to see how your model fares in a real-world scenario. This involves running the model through various benchmarks to assess its quality. You can think of this as your model’s report card, complete with scores that indicate how well it’s likely to perform. IBM’s Watsonx.governance, for example, manages these model cards to ensure a well-governed AI lifecycle.
4. Model tuning and refinement
If you’re wondering how to extract the best performance from your model, tuning is the answer. This entails generating specific prompts that will help your model respond better. Additionally, you might consider adding more localized data to refine its abilities further. Application developers often engage with the model at this stage, much like how Watsonx.ai functions within IBM’s workflow.
5. Model deployment
The culmination of all your hard work is the model’s deployment. Whether you choose to host it on a public cloud or integrate it directly into an application, the deployment stage is where your model is finally set free into the world. The beauty of AI is that this isn’t a ‘set it and forget it’ operation. Your model can—and should—be continuously improved over time.
To sum it up, the evolution of foundational models and workflow platforms has significantly accelerated the process of AI model development. With the five-stage process, you can navigate the complexities of creating both specialized and general-purpose AI models. The journey may be intricate, but remember, it’s a well-trodden path, filled with resources and community support to guide you every step of the way.
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