How To Build an AI Model
Building an AI model might seem complicated, but once you break it down, it becomes a lot more approachable.
Whether you’re a seasoned data scientist or new to machine learning, the workflow typically starts with understanding your data and goals and then advances through data preparation, model training, evaluation, and iteration.
In the past, you had to build everything from the ground up—collecting data, cleaning it, and developing your model from scratch. But things are changing. Now, with foundation models, you can start with a ready-made AI model and fine-tune it for your specific needs. This approach makes building models faster and more efficient.
So, what does it take to build an AI model today? Let’s walk through the main stages.
Stage 1: Prepare the Data
Every AI model relies on data, and the more data you have, the better—sometimes even reaching petabytes in size. You can gather data from open-source repositories or your own proprietary sources. But before you can use it, you need to get it organized and cleaned up.
Preparing Text Data
If your model will work with text, like analyzing documents or generating language, you’ll focus on processing text data. This process involves:
- Cleaning and Tokenizing: First, clean the text by removing any unnecessary characters or symbols. Then, the text is split into smaller parts, like words or subwords that the model can understand. This process is called tokenization.
- Stemming and Lemmatization: This step reduces words to their root forms. For example, “running” becomes “run.” This helps the model recognize different forms of the same word.
- Removing Stop Words: Stop words are common words (like “and,” “the”) that don’t add much meaning. Removing them helps the model focus on the more meaningful parts of the text.
Preparing Image Data
For computer vision tasks, which involve teaching machines to interpret and understand visual information from the world, image data needs its own set of preparations:
- Collecting and Cleaning: You’ll start by collecting relevant images. Low-quality or irrelevant images are removed to keep the dataset clean.
- Data Augmentation: Techniques like rotating, flipping, or cropping images add variety, which helps the model learn more effectively and perform better in real-world scenarios.
- Annotating: Annotation involves labeling parts of images (like tagging objects or marking regions). This helps the model understand what each part of an image represents.
Stage 2: Train the Model
Once the data is ready, it’s called the “base data pile.” This is a versioned and tagged dataset that keeps track of exactly what data and filters were used. With your data ready, the next step is to train the model using your base data pile. The first step in this stage is to select the appropriate foundation model. There are several types of foundation models available:
- Generative models for tasks like chatbots or text generation.
- Encoder-only models for tasks like classification, summarization, or pattern detection.
- Lightweight models for situations with limited computational resources, like mobile devices or IoT systems.
- High-parameter models for complex tasks that require deep learning and more powerful infrastructure.
If you’re working on Edge AI use cases, such as AI models running on devices with limited processing power like sensors, cameras, or smartphones, you’ll likely want to use lightweight foundation models. These are optimized for efficient performance on smaller devices, balancing accuracy and power consumption. After selecting the model that best fits your use case, you’ll then match your data pile with the chosen model.
Next comes tokenization. Foundation models don’t process raw data directly; instead, they break down words, images, or other inputs into smaller units called tokens. For text-heavy applications, this could mean millions or even trillions of tokens.
Now, you can begin the actual training process. This is where the model learns from the data by processing the tokens and adjusting its parameters. For large foundation models, training can be resource-intensive and time-consuming, sometimes taking months and requiring thousands of GPUs.
Stage 3: Validate the Model
Once the heavy lifting of training the model is behind us, we move on to validation. This is a crucial step where we assess how well the model performs in the real world.
First, we benchmark the model’s performance. This means running the model and comparing its results against a set of predefined benchmarks. These benchmarks help us measure key factors like accuracy, precision, and overall quality. It’s a way to make sure the model can handle the tasks it was built for.
After this, we create something called a model card. The model card is a summary that documents the model’s key information, including the benchmarks it achieved, its strengths, and any potential limitations. It’s a valuable reference that shows the performance of the model, and it offers transparency for future use.
Stage 4: Fine-Tune the Model
After validating the model, the next important step is fine-tuning. This phase is crucial for making sure the model works well for its specific purpose. Foundational models give us a solid starting point, but they’re usually trained on general datasets that don’t cover the specific details of every application.
Fine-tuning lets developers tweak the model for specific tasks and make it more relevant and accurate. For instance, a basic language model might need adjustments to understand and use specialized industry terms. This customization will improve the model and ensure it meets what users expect.
Stage 5: Deploy the Model and Monitor Its Performance
After testing and confirming that your model works as intended, it’s time to deploy it in the real world. This step is known as operationalizing the model, and it involves a few important actions.
First, you need to deploy the model and also set up a system to track its performance. Monitoring how the model performs in real time will help ensure it delivers the expected results.
Next, establish a baseline or benchmark to compare future versions of the model. This reference point is essential for assessing improvements or spotting issues over time. The goal is to keep enhancing the model by adjusting different aspects, like fine-tuning settings or changing data inputs, to improve its performance.
During this operational phase, consider factors such as versioning the model, making necessary updates, and the different environments where the model will be deployed. Depending on your needs, you can deploy the model in a controlled group for testing before a full rollout, in the cloud for flexibility and scalability, or on the edge for real-time processing.
Future-Proofing with embedUR
When building an AI solution, you should select a foundation model that’s optimized and aligned with both your hardware and long-term goals. Foundation models can vary greatly. Some are designed exclusively for high-powered cloud environments, while others are optimized for low-power edge devices. To make a good choice, look for essential hallmarks:
- Model size and parameter count
- Deployment environment compatibility (cloud versus edge)
- Performance on tasks critical to your use case.
If a model requires heavy computational power that only a data center can provide, it’s unlikely to perform well on a low-power edge device.
embedUR is here to ensure that you don’t just get a model but a complete, tailored solution. One that’s matched to your hardware, dataset, and platform. With ModelNova, we will get you to first base quickly with a solid proof of concept, laying the groundwork for scalable development.
However, moving from proof of concept to a full-scale, production-ready solution is a complex endeavor that requires careful planning. And choosing the wrong setup could mean costly redesigns or a short hardware lifespan.
Partnering with embedUR will also help you avoid these pitfalls. We’ll guide you in selecting models and platforms engineered for growth, so your AI can evolve over the next 2–3 years, allowing you to continually add value without costly reengineering.
With the right guidance from embedUR, you’re not just deploying a model; you’re building a robust, future-proofed AI solution that will scale with your vision. Check out our post on how Model Zoos are set to simplify your AI app developement.
