Creating quality onboarding from your data is crucial to the effectiveness of your ai system. This article will show you different approaches you can use to convert your data from formats like images, text, and audio into powerful embeddings that can be used for your machine learning tasks. Your ability to create high-performing embeddings will greatly impact the performance of your ai system, so it is essential to learn and understand how to create quality embeddings.
The motivation for this article is that creating good embeddings from your data is essential for most ai systems and is therefore something you should do often, which makes better embeddings a good way to improve all your future ai systems. Use cases for creating embeddings are tasks like clustering, similarity finding, and anomaly detection, all of which can greatly benefit from better embeddings. This article will explore two main ways of calculating embeddings; using an online model or training your own model, both topics will be discussed in later sections of this article.
· Introduction
· table of Contents
· Motivation and use case.
· Create embeds using PyTorch models
· Create embeds using HuggingFace models
∘ Approach 1
∘ Approach 2
· Create embeds using GitHub
· Create embeds using paid models
· Create your own embeds
∘ Autoencoders
∘ Training your own model in a subsequent task
· Typical mistakes when creating embeds
∘ Forget about using a pre-trained model
∘ License
· Conclusion
