# Data science and engineering Published articles for Data science and engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Search at Shopify--Range in Data and Engineering is the Future DevFeed: [Search at Shopify--Range in Data and Engineering is the Future]() Original publisher: [Read original article]() Author: Doug Turnbull Published: 2022-01-14T17:30:01Z Content type: opinion Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Shopify](), [Data Science](), [coding](), [machine learning research]() Tags: [data](), [data-science-and-engineering](), [engineering](), [search](), [search-and-discovery](), [shopify]() ### AI overview Shopify's search team treats range across data science and engineering as a core working principle. The article argues that combining both perspectives helps teams understand trade-offs, avoid silos, make better decisions, and deliver machine learning models to production. ### Source excerpt At Shopify, we draw very few lines between "data" and "engineering" work. Instead we have "search" work. ## How Target Performance Tunes Machine Learning Applications DevFeed: [How Target Performance Tunes Machine Learning Applications]() Original publisher: [Read original article]() Author: Target Brands, Inc Published: 2016-09-29T05:00:00Z Content type: article Language: en Sources: [Target]() Topics: [Machine learning](), [distributed-systems](), [Apache Spark](), [Hadoop](), [Scalability](), [Filesystems](), [datasets](), [Algorithms](), [Transactions](), [Open Source]() Tags: [algorithms](), [apache-spark](), [data](), [data-science-and-engineering](), [distributed](), [engineering](), [hadoop](), [machine-learning](), [open-source](), [scalability](), [science]() ### AI overview The article discusses improving the performance and relevance of machine-learning applications by evaluating matrix multiplication at scale. It describes distributed computing requirements, distributed file storage, scalability, fault tolerance, and the use of Apache Hadoop, Apache Spark, and Apache Mahout. ### Source excerpt At Target we aim to make shopping more fun and relevant for our guests through extensive use of data - and believe me, we have lots of data! Tens of millions of guests and hundreds of thousands of items lead to billions of transactions and interactions. We regularly employ a number of different machine learning techniques on such large datasets for dozens of algorithms. We are constantly looking for ways to improve speed and relevance of our algorithms and one such quest brought us to carefully evaluate matrix multiplications at scale - since that forms the bedrock for most algorithms. If we make matrix multiplication more efficient, we can speed up most of our algorithms! Before we dig in, let me describe some properties of the landscape we will be working in. First, what do I mean by large scale? A large scale application, at a minimum, will require its computation to be spread over multiple nodes of a distributed computing environment to finish in a reasonable amount of time. These calculations will use existing data that are stored on a distributed file system that provides high-throughput access from the computing environment. Scalability, in terms of storage and compute, should grow as we add to these resources. As the system grows larger and more complex, failures will become more commonplace. Thus, software should be fault-tolerant. Fortunately, there is a lot of existing open-source software that we can leverage to work in such an environment, particularly Apache Hadoop for storing and interacting with our data, Apache Spark as the compute engine, and both Apache Spark and Apache Mahout for applying and building distributed machine learning algorithms. There are many other tools that we can add to the mix as well, but for the purposes of this post we will limit our discussion to these three. With that out of the way, lets dig in! Don't Forget the Basics Begin with good old paper and pencil. Yeah, I know this is about large scale matrix operations that you c ## What Data Science Is and How It Supports Personalized Experiences DevFeed: [What Data Science Is and How It Supports Personalized Experiences]() Original publisher: [Read original article]() Author: Target Brands, Inc Published: 2016-03-01T06:00:00Z Content type: opinion Language: en Sources: [Target]() Topics: [Data Science](), [Machine learning](), [Algorithms, Complexity](), [High-Performance Computing]() Tags: [algorithms](), [data](), [data-science](), [data-science-and-engineering](), [engineering](), [high-performance-computing](), [machine-learning](), [science]() ### AI overview The article explains data science through everyday decision-making, describing how experience and instruction help people build models that guide judgment and anticipate outcomes. It connects these ideas to statistical models, machine learning algorithms, and Target's goal of creating personalized guest experiences. ### Source excerpt On my first encounter with it, around early 2010's, I was mystified. It sounded like witchcraft and I imagined the practitioners to be a coven of witches and wizards, all holding Ph.D.s in the dark art of "Data Science" and being respectfully addressed as "Data Scientists". It was believed they would magically transform haystacks into gold and then ask for your first-born in return as a reward for their service (a la Rumpelstiltskin) There is no denying the fact that the title "Data Scientist" is the most coveted one these days and has a nice ring to it. It's also true that data science has traditionally been a monopoly of mathematicians and statisticians. Obviously, developing statistical models and machine learning algorithms requires years of training and practice to specialize. In my opinion it is more of an art form driven by science and can easily be mistaken for magic. It's common knowledge that the more experienced in life we get, the easier it is for us to make up our mind. For instance, "What diner to pick for a boy's-night-out?", "When to stay off highways to avoid being stuck in a traffic-jam?", "When to buy a house? When NOT to buy?", are all such decions we make everyday. This ability comes as result of years of learning from implicit experience (a.k.a unsupervised learning) and explicit instructions from parents, teachers, friends, family and media (a.k.a supervised learning.) Our brain builds models of the world, of the situations we have been in, of banal and extraordinary, of nice and not-so-nice, of appropriate and inappropriate etc. These models facilitate judgment, govern behavior and enable anticipation of likely outcomes. That's basically data science. The recent progress in large scale and high performance computing has opened doors for such complex calculations to be performed on-demand and much more efficiently than was possible before. Hence, the buzz! At Target we operate in a guest-centric universe. We don't treat our guests as a statist