Artificial intelligence (AI)-based projects involve the creation and application of AI technologies to address particular issues or accomplish certain objectives. These projects often make use of deep learning models, natural language processing (NLP), computer vision, machine learning algorithms, or a mix of these methods.
Data Availability and Quality: AI models need a lot of high-quality data to train on. However, obtaining pertinent and labelled data might be very difficult. Poor quality, incomplete, or biassed data might result in subpar model performance. Additionally, it may be challenging to train reliable models in some domains due to the restricted availability of data.
Better Accuracy or Performance: AI models are made to learn from data and perform tasks or make predictions. Improvements in accuracy or performance over preexisting practises or baselines are frequently the main indicators of success. This may entail improved decision-making skills, quicker processing times, or improved forecast accuracy.