Recent Posts by Brittany-Marie Swanson

DataOps, or data operations, is the latest agile operations methodology to spring from the collective consciousness of IT and big data professionals. It focuses on cultivating data management practices and processes that improve the speed and accuracy of analytics, including data access, quality control, automation, integration, and, ultimately, model deployment and management. At its core, DataOps is about aligning the way you manage your data with the goals you have for that...

Over a decade ago, James Baurley, PhD, a software engineer by trade, and mathematician Carolyn Ervin, PhD, founded BioRealm, a genomic and artificial intelligence solutions provider that is helping researchers at institutions like the United States National Institute on Drug Abuse (NIDA) identify, on a large scale, genetic variants that can be linked to addiction. The company’s flagship product, Smokescreen, is being used by clinical researchers and healthcare providers to...

Data science is powering applications around the clock, from Netflix’s powerful content recommendation engine to Amazon’s virtual assistant Alexa. But not all companies are equipped to support large-scale, always-on data science projects; doing so requires an infrastructure that can handle the large volumes of traffic these models receive while in production, as well as a process for getting a finished model into a production environment. In their infancy, data science teams are...

For information technology (IT) teams managing the many applications required by large enterprise companies today, container runtime and orchestration tools like those offered by Docker, Kubernetes, and Cloud Foundry offer a great developer experience while helping to standardize security and optimize server utilization. Forrester Principal Analyst Dave Bartoletti reports that while only 10% of enterprise companies are currently using containers in production, up to a third...

As enterprise companies strive to become more agile, many are turning to DevOps as a way to deploy code quickly and efficiently. Unlike traditional IT, DevOps takes a holistic approach to fixing, updating, and deploying systems. It can also be used to successfully deploy data science models into production. But this format isn’t right for every business. To understand why larger companies are embracing a DevOps mentality, we sat down with Pam McCaslin, data scientist and...

Containers are lightweight versions of traditional virtual machines. They don’t take up large amounts of space on your server, they are easy to create and destroy, and they are fast to boot up. They also make creating repeatable data science environments easy. For a data scientist, running a container that is already equipped with the libraries and tools needed for a particular analysis eliminates the need to spend hours debugging packages across different environments or...

The phrase “deep learning” probably conjures up images of sentient robots staging a hostile takeover. But in reality, deep learning is just another way to describe large neural networks, a technology you encounter every day when you browse the Internet or use your mobile phone. Among countless other applications, deep learning generates captions for YouTube videos, serves up appealing food photos on Yelp, and answers iPhone users’ questions via Siri. And as data scientists and...

Sales slumps and spikes are a nearly unavoidable part of doing business. But if you’re ignoring when and why they happen, you’re missing out on opportunities to mitigate or leverage their effects. There’s a term for these predictable fluctuations: seasonality. The word “seasonality” is a really a misnomer: It implies that these patterns are related to seasons or the weather, when, in fact, they could be the result of any number of factors. For instance, a public company that...