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20 November 2019
6 min read
(Depiction of a clustering model, Medium) Getting started with machine learning starts with understanding the how and why behind employing particular methods. We’ve chosen five of the most commonly used machine learning models on which to base the discussion. AI taxonomy  Before diving too deep,...
19 November 2019
5 min read
Every year, millions of dollars are wasted planning, cleaning, and training machine learning (ML) models that will never get to production. This means that more than half of data science projects are not fully deployed—and some never will be, resulting in zero generated revenue. When organizations...
13 November 2019
3 min read
Meet with us! AWS re:Invent is next month, and we are pleased to announce that Algorithmia CEO, Diego Oppenheimer, will be speaking on the new software development lifecycle (SDLC) for machine learning. Often we get variations on this question: how can we adapt our infrastructure, operations, staffing,...
7 November 2019
5 min read
Why is churn prediction important?  Defined loosely, churn is the process by which customers cease doing business with a company. Preventing a loss in profits is one clear motivation for reducing churn, but other subtleties may underlie a company’s quest to quell it. Most strikingly, the cost of...
6 November 2019
3 min read
Read the case study Algorithmia is fortunate to work with companies across many industries with varied use cases as they develop machine learning programs. We are delighted to showcase the great work one of our customers is doing and how the AI Layer is able to power their machine learning lifecycle. Tevec...
31 October 2019
5 min read
If you’ve been keeping informed of what’s happening in the AI and machine learning world, you’ve probably heard a lot of talk about this nebulous thing called the cloud. While the cloud is often used to describe a variety of offerings for decentralized computing, there’s an underlying similarity...
24 October 2019
12 min read
We hear more and more everyday that businesses are sitting on troves of valuable data. It’s compared to precious metals, unrefined oil, or cash in a vault. But those items aren’t valuable simply because they exist. Their value comes from what is created out of them. The same holds true for data....
15 October 2019
5 min read
People often confuse data science and machine learning,  but they are in fact separate entities, despite what the memes say. Let’s make clear what differences there are between data science and machine learning and give some examples of how each is used in business settings. Is data science the same...
14 October 2019
4 min read
Machine learning engineers and data scientists are not the same role, although there is often the misconception that they are synonymous. While there are areas of overlap or reliance on one another, there are very distinct differences between these two roles in computer science.  What is a machine...
8 October 2019
4 min read
Customers have an abundance of options when it comes to products for purchase. This excess of options, however, increases the risk of poor customer retention. Since acquiring new customers costs much more than keeping current customers, a higher retention rate is always better. Customer retention represents...