This is a part of the series of blog posts related to Artificial Intelligence Implementation. If you are interested in the background of the story or how it goes:
➡️The term refers to scalability in expanding image dataset to be used in Machine Learning training process, and expansion or retraining of the machine learning model in scale with minimal effort. In simple terms, if you have a model that differentiates between a cat and a dog, you should be able to expand ai training easily by automatically collecting monkey images, and retraining, or expanding the existing classifier by using different frameworks. AI solutions with large models need effective workflows achieve model development.
How to reduce training time?
By using a clear data, you may reduce the noise and can have effective artificial intelligence models that can outperform other ai models. In computer vision, this aspect is more important since the workloads will change significantly when compared to algorithms responsible for nlp (natural language processing).
Easily Scraping Clear Data
On the previous week, I have shown how to get chips parameter manually from a Google search. SerpApi is capable of creating a list of different chips values and serve them under suggested_searches key.