10 Machine Learning Buzzwords You Need to Know

In the rapidly evolving field of machine learning, keeping up with the latest developments and trends can be a challenge. As new techniques and technologies emerge, so do new buzzwords and terminology. From deep learning to natural language processing, there is no shortage of technical jargon that can leave even experienced practitioners scratching their heads. In this article, we will demystify 10 machine learning buzzwords that you may have heard but may not fully understand. We will provide clear definitions and real-world examples to help you better understand these key concepts and stay on top of the latest trends in machine learning. Whether you are a seasoned data scientist or just getting started in the field, this article will provide valuable insights into some of the most important buzzwords in machine learning today.

Deep Learning

A subset of machine learning that uses neural networks with many layers to process and analyze complex data. It’s more data and resource-hungry in training, but it’s performance can make up for the cost.

Natural Language Processing (NLP)

A subfield of machine learning and linguistics that focuses on enabling computers to understand, interpret, and generate human language, including speech and text.

Semantic Embeddings

A type of representation of text, images or videos as vectors in a high-dimensional space, where the distance between these vectors reflects the semantic similarity between the corresponding objects.

User Modeling

The process of creating a representation of a user's characteristics, preferences, behavior, and needs in order to tailor and personalize their interactions with computer systems or applications.

Neural Networks

A computational model that mimics the structure and function of the human brain, used for processing and analyzing large amounts of data. The core tool behind machine learning.

Batch Inference

The process of using a pre-trained ML model to make predictions on a large batch of input data. In batch inference, the input data is collected and pre-processed before being fed into the machine learning model, which then generates predictions for all of the input data in the batch at once.

Real-time Machine Learning

The application of ML algorithms and techniques to data that is generated and processed in real-time or near real-time, as opposed to batch processing. Real-time ML systems are designed to quickly analyze and act upon streaming data as it is generated. We wrote a detailed article about real-time machine learning that can help provide more insight into the term.

Vector Search

Also known as similarity search, is a type of search algorithm used to find items or data points that are similar or related to a given query item or data point, based on their vector embeddings.

Recommendation System (RecSys)

A type of software that utilizes machine learning algorithms and techniques to suggest items or content to users based on their interests, and past behavior. The goal of a recommendation system is to provide personalized and relevant recommendations to users, thereby improving user engagement, satisfaction, and retention.

MLOps

A set of practices, processes, and tools that enable the deployment, management, and monitoring of machine learning models at scale. MLOps combines the principles of DevOps with the specific requirements of machine learning, such as the need for data versioning, model training, testing, and deployment.

In conclusion, staying up-to-date with the latest buzzwords in machine learning is essential for anyone working in the field. From deep learning to MLOps, these terms can be used to describe the latest technologies, techniques, and best practices in the industry. Did we miss any? Get in touch with us.