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Manceps

Introducing
AI Accelerate

Manceps designs and builds complex Machine Learning Infrastructure so your AI Researchers don’t have to.
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Building Machine Learning Models —

— requires advanced knowledge of highly complex algorithms.

On the other hand, putting those models into production requires a completely different skillset, particularly an in-depth understanding of ML requirements, systems configurations, and infrastructure design. 

Unfortunately, many AI researchers are on the hook for handling both, which we see as a total waste of their valuable time.

Our expert AI infrastructure architects can integrate a whole host of on-prem and in-cloud technologies to give you the foundation you need to build, train, and deploy artificial intelligence. 

Our deep knowledge of DevOps and Machine Learning gives our experts a unique ability to build integrated systems that are perfectly configured to meet your data science needs.

Companies that Separate AI from Infrastructure —

Access, deploy, and reuse datasets with ease.
Complete jobs and configurations faster.
Smoothly pipe data from ingestion to transformation to deployment.
Rapidly build and launch new models.
Reduce Management Overheads

Why Choose Manceps

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We are Platform Agnostic

Manceps is committed to helping you choose and configure the best platforms to help you achieve your goals, regardless of which company makes them.
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Our Solutions Have Many Integrations

Whether a fan of SageMaker or Azure, Alooma or Stitch, every AI developer has their own toolkit preferences. Our Infrastructure solutions play nicely with a whole host of tools, which means your team will be able to work exactly how they want.
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We Build for Scalability

As demand and resource needs fluctuate, IT teams must have the flexibility to expand or contract their infrastructure. Whether in the cloud or on-premises, our flexible solutions scale effortlessly so your IT can grow just as fast as your business.
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We Help Your Team Succeed

After configuring your stack, we can provide your team with all the training it needs to efficiently manage your configuration.

With AI Infrastructure, your machine learning experts can stop worrying about configurations, schedules, or resource allocation and focus on what they do best — building and deploying AI models.”

— Al Kari, CEO of Manceps


Timeline and Scope

1-2 Weeks

Algorithm Selection

After capturing your requirements, our team will research, benchmark, and select the best algorithm for your application.
1-2 Weeks

Feature Engineering

Manceps will identify the most relevant inputs (features) from which to derive the insights you want your model to produce. These multi-dimensional data points can come from within your company and, if necessary, augmented with publicly available datasets.
1-2 Weeks

Model Development

Finally, we will train, fine-tune and validate a model that puts your data to work and achieves required accuracy metrics.

Get Started.

OUR LATEST RESOURCES
OUR LATEST ARTICLES

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