Algorithmia launches Teams edition to help companies get machine learning applications into production faster and more cost effectively.

Learn about CI/CD pipelines, how they improve the software development lifecycle, and Algorithmia’s CI/CD solution for machine learning.

Semi-supervised learning is the type of machine learning that uses a combination of a small amount of labeled data and a large amount of unlabeled data to train models. This approach to machine learning is a combination of supervised machine learning, which uses labeled training data, and unsupervised...

Computing has the power to do some of the things that the human brain can do, thanks to advances in artificial intelligence. One of those advances is text processing, which also relates to natural language processing. This article is a deep dive into what text processing is and how it can generate value...

A data lake is a centralized repository of all an organization’s data stored in its raw format. This allows enterprises to store all their data, in its natural or raw state, in one location. This includes structured, relational data with rows and columns, semi-structured data such as CSV or XML files,...

The field of natural language processing (NLP) is concerned with the creation of machine learning methods for understanding written and verbal data. And as in any subfield of machine learning, it’s necessary to devise a technique for creating numerical representations of that data so it can be acted...

Applied machine learning is the application of machine learning to a specific data-related problem. This machine learning can involve either supervised models, meaning that there is an algorithm that improves itself on the basis of labeled training data, or unsupervised models, in which the inferences...

Data democratization, the process of allowing as many people as possible to have access to data without any bottlenecks or gatekeepers, can happen both within and between organizations. Within an organization, data democratization might mean that the IT department makes data easily and readily accessible...

One of the final (and arguably most important steps) in developing a machine learning model is evaluating its accuracy. You can’t trust a model to make good predictions about new and unknown data if it’s struggling with training data. Regression models evaluating accuracy usually means calculating...

Time series decompositions are one of the most important forms of data in machine learning and break down a series of events over time into analyzable components. Examples of data that might form a time series include the prices of stocks at various times, the number of passengers flying on an airline...