Machine learning

Algorithms that independently learn to make predictions and decisions after training on datasets instead of being explicitly programmed.

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14 July 2020
5 min read
There are many metrics via which one can measure the performance of a model. One possible measure is the mean absolute percent error. It is calculated by taking the mean of the absolute value of the actual values minus the predictions divided by the actual values. Another measure of performance is the...
10 July 2020
5 min read
In machine learning, a parametric model is any model that captures all the information about its predictions within a finite set of parameters. Sometimes the model must be trained to select its parameters, as in the case of neural networks. Sometimes the parameters are selected by hand or through a simple...
7 July 2020
8 min read
In machine learning, model validation is the process of verifying that models are providing a satisfactory fit to their data, in line with both qualitative and quantitative objectives. While partially consisting of a set of tried-and-true processes, model validation is a heterogenous process that cannot...
30 June 2020
5 min read
Artificial intelligence (AI) and machine learning (ML). It’s likely that you’ve heard both of these terms with increasing frequency over the past few years, often in the context of big data. You may have also noticed that they’re often used interchangeably, which is erroneous.  In short, machine...
24 June 2020
4 min read
Software engineers and data scientists are two distinct, yet equally important roles in computer science. Although they both require knowledge of programming, there are several differentiating factors between software engineers and data scientists. Software engineers specialize in the creation and maintenance...
11 June 2020
5 min read
Fundamentally, machine learning models are divided into two camps: supervised and unsupervised. The supervised model is probably the type you’re most familiar with, and it represents a paradigm of learning that’s prevalent in the real world.  What is supervised learning?  In supervised learning,...
4 June 2020
3 min read
Big data is a field that was developed for organizations to process, analyze, and extract information from datasets that are too large for traditional data collection methods. Today, we will be talking about the big data industry, including what kinds of industries use this data, real world examples...
2 June 2020
3 min read
As your company begins to proof out machine learning use cases and develop models, your ML teams need to be thinking long-term. How will you deploy, operate, and manage your models once you have them? Making AI–minded decisions like this starts with this question: should I build or buy a machine learning...
28 May 2020
3 min read
At Algorithmia, we have learned how enterprise machine learning is developing across industries, and we are most interested in the challenges that hinder organizations from extracting ML value. In late 2019, we published data on the main challenges, which we consolidated in a report called the “2020...
26 May 2020
8 min read
The machine learning lifecycle begins with data warehousing, ETL pipelining, and model training. At Algorithmia, we focus on the next stages in the lifecycle: deployment, management, and operations. Machine learning deployment plays a critical part in ensuring a model performs well, both now and in the...