What is hardcore data science – in practice?


For example, for personalized recommendations, we have been working with learning to rank methods that learn individual rankings over item sets. Figure 1: Typical data science workflow, starting with raw data that is turned into features and fed into learning algorithms, resulting in a model that is applied on future data. This means that this pipeline is iterated and improved many times, trying out different features, different forms of preprocessing, different learning methods, or maybe even going back to the source and trying to add more data sources. Probably the main difference between production systems and data science systems is that production systems are real-time systems that are continuously running.

10 Ways Machine Learning Is Revolutionizing Manufacturing


Bottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production. Machine learning's core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

Artificial Intelligence is now Intel's major focus


With technology governing almost every aspect of our lives, industry experts are defining these modern times as the "platinum age of innovation"; verging on the threshold of discoveries that could change human society irreversibly, for better or worse. At the forefront of this revolution is the field of artificial intelligence (AI), a technology that is more vibrant than ever due to the acceleration of technological progress in machine learning - the process of giving computers with the ability to learn without being explicitly programmed - as well as the realisation by big tech vendors of its potential. One major tech behemoth fuelling the fire of this fast moving juggernaut called AI is Intel, a company that has long invested in the science and engineering of making computers more intelligent. The Californian company held an'AI Day' in San Francisco showcasing its new strategy dedicated solely to AI, with the introduction of new AI-specific products, as well as investments for the development of specific AI-related tech. And Alphr were in town to hear all about it.

Questions To Ask When Moving Machine Learning From Practice to Production


With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings. Coupled with breakneck speeds in AI-research, the new wave of popularity shows a lot of promise for solving some of the harder problems out there. That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks. A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved. This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production.

Large-scale machine learning at Criteo


At Criteo, machine learning lies at the core of our business. We use machine learning for choosing when we want to display ads as well as for personalized product recommendations and for optimizing the look & feel of our banners (as we automatically generate our own banners for each partner using our catalog of products). Our motto at Criteo is "Performance is everything" and to deliver the best performance we can, we've built a large scale distributed machine learning framework, called Irma, that we use in production and for running experiments when we search for improvements on our models. In the past, performance advertising was all about predicting clicks. That was a while ago.

Amazon Joins Tech Giants in Open Sourcing a Key Machine Learning Tool


"DSSTNE (pronounced "Destiny") is an open source software library for training and deploying deep neural networks using GPUs. Amazon engineers built DSSTNE to solve deep learning problems at Amazon's scale. DSSTNE is built for production deployment of real-world deep learning applications, emphasizing speed and scale over experimental flexibility. "Deep Scalable Sparse Tensor Network Engine, (DSSTNE), pronounced "Destiny", is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models.