Monitoring machine learning models is an essential part of the development process, but it can be easy to overlook specific vital considerations. A checklist helps keep track of everything you need to consider when monitoring your models and can help ensure that your models perform at their best.
Data leakage in machine learning refers to including information in the training data that would not be available at the time of prediction. This can lead to overfitting and poor generalisation because the model has been trained on data that would not be available at runtime.
Transfer learning is a machine learning technique that uses a pre-trained model as a starting point to build a new model rather than training a model from scratch. This can be useful when there is a limited amount of labeled data available or when the problem at hand is similar to a problem that has already been solved using machine learning.
This year has been the year of generation for Artificial Intelligence. Over the years, AI has worked and tried to perfect the age of content and artistic marvel, through which OpenAI has been leading the charge. With their models DALL-E 2 and the talk about GPT-4, we saw a bunch of labs pushing their models in the sphere, which gave us models like Stable Diffusion and GATO coming to light. However, as the year ends, OpenAI unveils another textual model, which brings AI for text generation out to the public.
Training a deep learning model, such as the MobileVIT model, can be a complex and challenging task, but it is also a powerful and effective way to solve a wide range of problems in computer vision and other fields.
