This next video is on inferring. I like to think of these tasks where the model takes a text as input and performs some kind of analysis. This could be extracting labels, extracting names, or understanding the sentiment of a text. In traditional machine learning workflows, you have to collect the labeled dataset, train the model, figure out how to deploy the model somewhere in the cloud, and make inferences. This can be a lot of work for each task. However, with large language models, you can just write a prompt and have it start generating results right away. This gives tremendous speed in terms of application development and allows you to use one model and API to do many different tasks. For example, you can extract sentiment, positive or negative, of a piece of text by simply writing a prompt. You can also extract emotions or specific information from a customer review, such as the item purchased and the brand. Large language models are also capable of inferring topics from a long piece of text, which can be useful for understanding the content of news articles or customer reviews. Overall, large language models have revolutionized natural language processing tasks and made them more accessible to both experienced machine learning developers and newcomers to the field.
