By 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 1 (Noodle | Medium) and Part 2 (Noodle | Medium) be before proceeding further.

Let’s consider a problem of anomaly detection from multi-variate time series data. Let’s say, we have one month of time series data available at a 10-millisecond granularity. …

by Prateek Mishra, Director of Engineering,

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 we went over the challenges associated with developing Enterprise AI applications. These problems can be mapped into 3 broad categories, which can be thought of as independent axes. …

by Prateek Mishra, Director of Engineering, 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, explaining the factors that were considered, choices that were made and the benefits that we derived out of it. In the first part the focus will be on 4 high-level factors which contribute to complexity of building an AI application and we will dive into details of each of these:

by 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, and autoregressive time-series modeling¹. The typical uses of “associative” AI might be still effective in simple diagnostic or predictive contexts where we want to use probabilistic associations like correlations between observed features and targets (e.g., When we observe variable X, how likely we would be to observe variable Y?), but…

How it’s Done

by Matt Denesuk, Ph.D., Chief Data Science Officer, Noodle.AI


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 things that humans can already do very well (things like facial recognition, natural language understanding, driving a car). Enterprise and Industrial AI problems (e.g. problems in supply chain planning or industrial operations management) tend to be much less mature and require different approaches, skills, expectations, and planning. …

and 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, which began their transformations at the dawn of the computer age, are among the earliest examples of industries transformed. Since then we’ve seen many more examples in telecom, media and entertainment, retail, and other industries. …

Applying #AI and #ML to the toughest business challenges. LinkedIn 2018 #1 B2B Startup. Your source for Enterprise AI®

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