Image search relies on many different types of data – some sourced from image creators, some developed by machine learning models, some extracted from the surrounding image context. Different data is optimal for different search models, but what data is best for the user? This talk will discuss leveraging machine learning in image search, while keeping the user top of mind.
This session is about how to train models on the cloud without changing a single line of code. From model development to hyperparameter sweeps, Grid automates all the engineering, so you can focus on machine learning instead of getting bogged down in infrastructure bottlenecks. In this session, William will walk you through the general workflow of research, from creating a hypothesis on a model on jupyterlab or on SSH to training a large-scale network using multiple GPUs simultaneously without having to change a single line of code.
Machine Learning Strategies for Kaggle vs. Industry