Acquiring a dataset is just the start when developing a Computer Vision model, with the real challenge being its refinement for optimal performance. Low-quality, bloated datasets can waste resources and negatively affect model performance, making Active Learning pipelines crucial for effective curation. Active Learning allows teams to intelligently select data that significantly impacts the model's performance by focusing on the model's current needs, which ensures that each data point is impactful. This approach leads to a more streamlined annotation process and results in a more accurate and efficient Computer Vision model. A case study highlighted in the webinar shows that one customer was able to increase their mean Average Precision (mAP) by 20% while reducing the dataset size by 35% through visual data curation.