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Manceps

Manceps Delivers AI-Powered Quality Assurance Solutions to Manufacturers

Let’s face it, maintaining a rule-based system for quality assurance is complex, time-consuming, and prone to inaccuracies.
 
By replacing this resource-intensive process with artificial intelligence, you ensure that every defective item coming off of the factory floor is properly identified, every time.
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Our AI solutions integrate with manufacturing's most popular technologies.

Roll-out QA system-wide

With human operators, quality assurance can only be done at certain checkpoints throughout the production process. By contrast, an AI-powered quality assurance system can monitor the production throughout, making it easier to not only identify defects but also to pinpoint the cause of each defect.
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Go Further with Optical Grading

With optical grading, you can go beyond black and white labeling to capture the nuance of your goods across a spectrum. For example, a produce manufacturer may want to sort across different levels of freshness. A timber processor could sort across wood type or grain quality.

Use NLP to Ensure Proper Labeling

Did you know that allergenic products, such as dairy or nuts, not declared on the label are a major cause of product recalls? Using Natural Language Processing, manufacturers can inspect every label for accuracy before it leaves the building.
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“Automating quality testing using machine learning is increasing defect detection rates up to 90%.”

— Louis Columbus, Forbes

How It Works

At Manceps, our AI-powered analytics are customized to your individual products and needs, ensuring that your products are produced to your and your customers’ specifications, quickly and accurately, shortening latency and minimizing rework or waste. Unlike traditional rules-based systems, artificial intelligence learns from examples.

Step One.

First, we identify the data sources such as historians, edge devices, smart camera vision systems, and data stores that we'll use to build and train your models. This data will give us enormous insight into what you're monitoring and where your issues are.

Step Two.

Next, we assign every item in your dataset one of two labels: quality and non-quality.

Step Three.

Once we’ve gathered and labeled enough examples, we use this dataset to train an AI model. In this case, training an AI model means instructing it to develop an intuition about the differences between the two categories.

Step Four.

Once trained, we then roll-out your model to the factory floor. Using the inputs, we configure the AI to spot and label any defects in real-time.

Step Five.

Upon labeling a product non-quality, various systems can use this information in all sorts of ways, depending on your needs. For example, in a fully automated system, a robot could be configured to remove the product. In a semi-automated solution, a worker could see the defective item highlighted on a computer screen.

Step Six.

By identifying items that are mislabeled by the system, the AI model, over time, will become increasingly precise, sophisticated, and nuanced.

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DevFest West Coast 2020

Watch videos of some of the world's top AI experts discuss everything from Tensorflow Extended to Kubernetes to AutoML to Coral.

Video: Machine Learning Engineering with Tensorflow Extended

In this talk, Hannes is providing insights into Machine Learning Engineering with TensorFlow Extended (TFX). He introduces how TFX for machine learning pipeline tasks and how to orchestrate entire ML pipelines with TFX. The audience learns how to run ML production pipelines with Kubeflow Pipelines, and therefore, free the data scientist's time from maintaining production machine learning models.

Video: How to Build a Reproducible ML Pipeline

Solving a data science problem usually requires multiple steps. These steps can include extracting and transforming data, training a model, and deploying the model into production. In this session, we'll discuss how to specify those steps with Python into an ML pipeline. We'll show how to create a Kubeflow Pipeline, a component of the Kubeflow open-source project. The audience will learn about how to integrate TensorFlow Extended components into the pipeline, and how to deploy the pipeline to the hosted Cloud AI Pipelines environment on Google Cloud. The key takeaway is how to improve reuse and reproducibility of the machine learning process.

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OUR HEADQUARTERS
Headquartered in the heart of Portland, Oregon, our satellite offices span North America, Europe, the Middle East, and Africa.

(503) 922-1164

Our address is
US Custom House
220 NW 8th Ave
Portland, OR 97209

Copyright © 2019 Manceps