Embedding: Why Every ML Engineer Should Know This Versatile Technique

All that glitters is not gold. In reverse, you can also say that all that looks plain and boring is not dross, and the same can be said of the embedding technique in Machine Learning (ML). Mildly esoteric and abstract, the concept is often given the backburner. However, embedding helps ML engineers build practically anything – from chatbots to recommendation engines, search bars, and more. So, what is embedding? You may ask. And keep reading to find that out and more.

What Is It?

In purely technical terms, embedding is a low-dimensional space in which one can translate high-dimensional vectors. In simpler terms, embedding is a technique to translate from one context to another.

For instance, in the case of a self-driving car, the image is first translated into an embedding. After that, the engineer can decide what is to be done with the embedded context. The first-ever embedded system was released for a vehicle back in 1968. Likewise, the technique of ML embedding does share a rich history.

What Can Embedding Help Build?

Now that you know the answer to “what is embedding?” let’s dive into the different things embeddings can help build in the virtual space (practically anything!).

Recommendation systems and engines – For instance, when you’re shopping online or watching movies on Netflix, you get the option of “You Might Also Like” or “Based on Your Past Purchases” recommendations. These are developed using ML embedding.
All types of searches – This includes music search on music apps, image search like Google images, and text search on a search engine like Google search.
Chatbots and other systems that answer user queries and doubts
Data pre-processing, which includes feeding data into an ML model
Outlier or fraud detection systems
Typo detection systems
Zero-shot or one-shot learning models, which include ML models that can learn quickly without any training
Drift or detection of stale ML models

And a whole lot more! In any case, the usability of embedding is too vast to be covered in a single section. This is perhaps one of the main reasons ML engineers should invest their time in learning this technique.

What Can Be Embedded?

Just like embedding can build practically anything, it can also use anything for its purposes. This means you can embed both text and images. And in the case of text, individual words and entire sentences and chunks of text can be easily embedded.

As for images, embedding enables reverse-image search or “search by image.” So, let’s take an example of a clothing store. If you want to build a search feature for the store, you need to support the same using text embedding for statements like “sequin-studded mini skirt,” among others. And once the same is typed into search, the system matches against product descriptions and shows all miniskirts studded with sequins. However, a neater way to go about this would be to include a search-by-image option.

Under this feature, shoppers could simply find what they’re looking for by uploading a trending image, perhaps from Instagram.

Making Complex Data Simple Begins Here!

High-dimensional data is more often than not challenging to use, train, analyze, or plot. This is where the power of embedding steps in.

So what is embedding? It is how the number of data dimensions can be reduced. Naturally, this simplifies the tasks of an ML engineer significantly. If you’re an aspiring ML engineer, embedding is one technique you ought to learn!

Author name- Grace