AI Model Training Best Practices: Tips for Optimising Performance
Understanding AI image generation is complex, with a vast difference between using an AI generator with your images to add prompts, text descriptions, and details and programming an AI model to provide flawless performance. There are many different aspects of training an AI model and utilising Stable Diffusion model learning resources, alongside other tutorials and online guides from AI text-to-image providers, can be a useful starting point.
Let’s run through some basic requirements, the best practices that reduce the potential for errors, and some of the technical metrics your AI model should be able to recognise, understand, and explain.
How Do I Train an AI Model for High-Quality Outputs?
AI is now being used in a vast array of applications, with huge uptakes across industries, where businesses and developers look for new ways to use the potential of AI to perform tasks and automation in a fraction of the time it would otherwise take.
The first element is to ensure you have the hardware capacity and operating power to cope with deep learning or machine learning models. In most cases, you’ll need at least a GPU with 6GB VRAM, plus 10GB of storage space in addition, often through a hard drive or another storage device.
However, improving AI models or training new models to leverage the responsiveness and agility of AI algorithmic computations is not straightforward and will also depend on the nature of the model–whether it’s a deep learning model used in audio, image, video, and text generation, or a structure data model which bases outputs on data and series.
Improving AI Models in an Emerging Space
The biggest challenge for anybody training an AI model is that this area is still in its infancy, and no foolproof processes or structures will guarantee any model will work perfectly from day one. Instead, developers and programmers can train models using established best practices, which makes it less likely their AI model will fail to deliver or not provide the specificity of outputs expected.
Model performance relies on accuracy and technicalities, so before beginning model training, it makes sense to pin down the exact deliverables you are aiming for, such as:
- The use case: Would you like your AI model to produce forecasts, search results, artwork, text content, or something different?
- Metrics involved: What inputs are you going to train your model in? This could be audio, numerical, unstructured, text, video, or categorical data.
- How will your AI model use parameters and techniques to decide which outputs match the question or command?
These are all big-picture factors, but investing time into the initial planning stage can help identify constraints or technical limitations, which may need to be addressed before the model can be trained and deployed.
Preparing and Controlling Datasets
The quality and volume of data you provide your AI model will be fundamental, so collecting and preparing data is essential, ensuring you have already defined the goals of the model, established the way you will store and collate new data, and evaluated the data to verify it meets your quality benchmarks. From there, you should process your data through:
- Removing low-quality or limited relevance data from the dataset
- Adding annotations and labelling before training
Selecting the most suitable AI model is the next phase, where your model needs to have the right algorithms and architecture to meet your requirements. There are countless options, from decision trees to neural networks, but your choice should be based on the size and data structure, the accuracy needed, and the complexity of the problem your AI needs to solve.
Initial training means you input the data and work through theoretical questions to identify errors and test outputs in different environments. As issues arise, you can refine the datasets by augmenting, expanding the data, or simplifying the AI model until it begins to return more relevant results.
Validating AI Model Training
When your initial training has concluded, you progress to validation, verifying whether your assumptions about how your AI model performs are corroborated when you input a new validation dataset. The results show any issues, gaps, or problems, allowing you to make adjustments as necessary.
Only when the initial dataset preparation, training, and validation are complete is your AI model ready for testing with real-time or real-world inputs, making sure it performs to meet your aspirations before being deployed.