AI model optimization refers to the process of refining the model’s algorithms to ensure they are ready to be used for real-world applications. It includes refining algorithms to enhance their performance, reduce computational cost, and improve their accuracy and efficiency. There are various AI optimization strategies to achieve this as explained in the following infographic.
But when it comes to optimizing an AI model, the most important aspect is reducing its size without compromising its efficiency and accuracy. A recent study by Stanford found out that the average size of large language models has been decreasing. There is almost a 90% reduction in parameters between May 2022 to April 2023. This number indicates how organizations are giving more importance to AI and ML model efficiency and how easily they can be deployed.
Additionally, optimization also includes several other parameters such as solving issues like overfitting and underfitting. As we enter the future, we might see AI becoming more pervasive, and there will be a need to optimize models for specific hardware, platforms, and devices.
In the following infographic, discover what are the various components of AI model optimization, check out various AI model optimization strategies followed by AI engineers, and explore the benefits as well as challenges of AI model optimization. Having a basic understanding of this process might immensely help you in your AI career.
