Questions To Ask When Moving Machine Learning From Practice to Production

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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.


Questions To Ask When Moving Machine Learning From Practice to Production

#artificialintelligence

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.


Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019

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The insane pre-holiday shopping is behind us, along with celebrations, and personal to-do lists for the next 12 months. So, let's analyze the data science and artificial intelligence accomplishments and events of the past year. We talked with experts from Booking.com, Wolfram Research, BetConstruct, and other data science specialists who shared their thoughts about opportunities as well as their influence on business, research, and everyday lives for both industries. Experts have different points of view on whether 2018 was rich in important achievements and events. No recent achievements can compete with inventions of a multilayer perceptron (MLP), neural net training techniques like backpropagation and backpropagation through time (BPTT), residual networks, the introduction of Generative Adversarial Networks (GANs), and deep Q-learning networks (DQN). "So, looking back to memorable ones I listed before, there weren't'brand new' accomplishments in 2018," summarizes Oleksandr.


Deep Learning in Production for Predicting Consumer Behavior – Zalando Tech Blog

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At Zalando adtech lab in Hamburg, machine learning drives many of our production systems to build great user experiences. Our most recent product requires precise estimates of future interests of Zalando consumers based on their history of interacting with the fashion platform. For example, we want to predict a consumer's interest in ordering selected fashion articles. We set ourselves the goal to build a powerful and versatile prediction tool that not only fits the task at hand, but is also ready for future product developments. Deep learning approaches have many advantages over traditional techniques, making them a great fit for our requirements.