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Manceps

OUR LATEST ARTICLES

Ask the CEO: How Does Manceps Work with Its Customers?

Ask the CEO: How Does Manceps Work with Its Customers?

Transcript:

Think about the technologies we're building. Think about all of these solutions that are coming out so fast. Everything is evolving very quickly.

When you talk about Tensorflow, for example—a major framework that's being used for building AI systems today—Tensorflow had a major release every two months for the past two years. It's moving so fast and it's it's really building to deliver significant value by adapting some of the libraries that are already built. And a lot of that is moving much faster than a lot of companies can adapt to.

The way we operate, we help customers by initially introducing them to A.I. concepts and how that could work. If they are in a greenfield and they haven't gotten any AI yet, we start by optimizing some of their systems with some AI systems and that could work with some of their data.

We also put that into a learning mode and start slowly moving them and to optimize it, optimize an augmented fashion. So as they're as they as their new systems are optimized, they start learning from their human operators, they get to incrementally start augmenting.

One day, when we have enough training data and when the systems are delivering enough confidence, we flip the switch. When we flip that switch, the human operator stays. The person continues to actually operate the system, but only for anomalies and for exceptions. And the AI system handles the the majority of the workload.

That allows the human operator to go on to doing better and bigger things, more important things, things that require more creativity, and more thought out processes.

20.11.2019

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Our Most Recent Articles

DevFest West Coast 2020

DevFest West Coast 2020

Video: Machine Learning Engineering with Tensorflow Extended

Video: Machine Learning Engineering with Tensorflow Extended

Video: How to Build a Reproducible ML Pipeline

Video: How to Build a Reproducible ML Pipeline

Video: ML adventures with AutoML and TFHub

Video: ML adventures with AutoML and TFHub

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50 AI Secrets: How Every Fortune 50 Company is Using AI Right Now

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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

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