Jun 13, 2021Deep Probabilistic Decision Learning Returns Perfect Flow to OperationsBy: Hyungil Ahn, SVP & Chief AI Scientist, Noodle.ai FlowOps enables optimal experience, predictions and decisions in the operations of factories and supply chains. In human brains, there are three key learning functions related to how we sense, predict and decide. Findings in computational neuroscience [1, 2] suggest that different…Dpdm5 min readDpdm5 min read
Sep 1, 2020Atlas, Noodle.ai’s Machine Learning (ML) Framework Part 3: Using Recipes to Build a ML PipelineBy Rupal Sonawane, co-authored with Prateek Mishra Part 2 in this series provided an introduction about what Recipes are and the potential benefits. In this blog, we will take a deep dive into a Recipe implementation contrasting with the typical Machine Learning (ML) pipeline implementation. We recommend reading Atlas Part…Mlops11 min readMlops11 min read
Jul 24, 2020Atlas, Noodle.ai’s Machine Learning (ML) Framework Part 2: Design Premise & Architectureby Prateek Mishra, Director of Engineering, Noodle.ai To design a system the first step is to understand the problems and the constraints. Each architecture is unique in the way it solves for these and there is usually more than one solution. In the Part 1 (Noodle Blog/Medium) of this blog…Artificial Intelligence8 min readArtificial Intelligence8 min read
Jul 13, 2020Member-onlyAtlas, Noodle.ai’s Machine Learning (ML) Framework Part 1: Challenges With Building AI Applicationsby Prateek Mishra, Director of Engineering, Noodle.ai Noodle.ai is focused on providing AI products to address a diverse set of problems which could aide in better visibility, decision making and waste reduction for our Enterprise customers. This two-part blog covers the journey of building our in-house ML framework — Atlas…Machine Learning8 min readMachine Learning8 min read
Published inNoodling on The Future of AI·Sep 19, 2019Deep Probabilistic Decision Machines for Building a Causally Generative Process Model-based Action Control in Enterprise AIby Chief Scientist, AI and Data Science, Hyungil Ahn, Ph.D. and the AI R&D team There are many machine learning libraries and automated tools that might be applied to enterprise AI problems. We find that these tools tend to support only the “associative” AI problem setups such as regression, classification…Machine Learning16 min readMachine Learning16 min read
Published inNoodling on The Future of AI·Nov 4, 2018Enterprise & Industrial AI ModelingHow it’s Done — by Matt Denesuk, Ph.D., Chief Data Science Officer, Noodle.AI Introduction My earlier paper¹ described how Enterprise & Industrial AI differed from more popularized applications of AI. The latter tended to be more “mature” (i.e. at the later stage of creating solutions to the problem) and also tended to be targeted at…Machine Learning15 min readMachine Learning15 min read
Published inNoodling on The Future of AI·Oct 6, 2018What’s Different about Enterprise & Industrial AIand Why Does it Matter? — by Matt Denesuk, Ph.D., Chief Data Science Officer, Noodle.AI Why does AI matter to industrial firms and traditional enterprises? Throughout modern business history, industries that have become substantially all about “data + math” have seen both enormous gains in productivity as well as disruption of business models and business leadership. Today, we call this digital transformation. Financial services and banking…Machine Learning9 min readMachine Learning9 min read
