Special topics in machine learning
Algorithmia is machine learning, managed. Deploy, serve, and manage your ML portfolio.
11 September 2020
4 min read
A machine learning framework is an interface that allows developers to build and deploy machine learning models faster and easier. A tool like this allows enterprises to scale their machine learning efforts securely while maintaining a healthy ML lifecycle. read more
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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...
1 July 2020
3 min read
Today we are excited to showcase several new features and upgrades to the Algorithmia Enterprise platform, most notably around management and governance of machine learning systems. Repost of Aithority press release: Algorithmia, a leader in ML Operations & Management, announces a series of upgrades...
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...
22 June 2020
4 min read
Today, mass amounts of data come from a myriad of applications and microservices. DevOps engineers are often tasked with ensuring that data is collected, retained, and secured in a way that follows strict regulations. Focusing on data security, many companies rely on VMware for various internal cloud-computing...
18 June 2020
6 min read
On 9 June 2020, Algorithmia CTO, Kenny Daniel, co-hosted a webinar with Sam Charrington of TWIML on building internally versus buying an existing machine learning operations platform. The webinar recording can be accessed here. The discussion generated a lot of questions, and Kenny, Sam, and other Algorithmia...
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,...
9 June 2020
7 min read
Deep learning is a subset of machine learning that deals with algorithms that mimic the function of the brain, called artificial neural networks, which learn from large sets of data. It is called “deep” learning since it uses multiple layers in a network, making it deeper than other more simple...