IntrospectData Blog

Learnings, helpful information and what's going on here at Introspect!




Cloud

How to Reduce Costs With DevOps in 5 Steps

01/18/2020 10:21 a.m.

Whether your organization is new to the concept of DevOps or if you simply haven’t noticed more room in your budgeting after implementing it, we have some ideas for you – five to be exact. Whether you need to invest dollars or simply more attention toward the cloud, DevOps can make the process easy and save money while doing it. And lastly, they give DevOps teams the ability to switch between programming environments quickly because you can run nearly any app built in any language inside containers.




Cloud

Implementing a DevOps Strategy the Right Way

01/18/2020 7:34 a.m.

But this cultural change should be considered an asset because giving DevOps an environment to thrive results in improved speed and agility for the organization. Since DevOps was merely a buzzword a few years ago, there hasn’t been time to develop a career path or college coursework that funnels people into these positions. Other valuable skills brought to this position include an understanding of SDLC, infrastructure awareness, experience in both software development and operations, and the ability to keep up with changing technology.




Data

The Difference Between Supervised and Unsupervised Learning

01/16/2020 11:12 a.m.

In broad terms, a supervised learning model seeks to teach a machine to follow a set of parameters based on information that is already known. Now take that classroom scenario and change it so that the only instructions given are to ‘organize these items.’ As a result, we’ll have several different groupings based on any number of characteristics. As machines learn to teach themselves based on experience (as humans do), further applications in multiple fields will become apparent.




Data

Factorization Machines for Machine Learning

12/05/2019 1:20 p.m.

A Google research scientist, Steffen Rendle, introduced Factorization Machines in one of his papers in 2010. SVMs use dense parametrization and their computation of a prediction relies on the training data, or support vectors. * They depends on a linear number of parameters * FM doesn’t rely on training data, resulting in more compact models.