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Manceps
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As a trusted Google Partner with data analytics and machine learning specializations, Manceps has the knowledge and experience to help you deploy artificial intelligence across Google Cloud
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A Natural Alignment

By now, most enterprise organizations have come to depend on the scalability, reliability, and adaptability of cloud infrastructure. What they might not realize is that cloud infrastructure is excellent for deploying artificial intelligence models and warehousing data.

How Manceps brings AI to Google Cloud Platform

Prepare

Manceps can help you use Google Cloud Storage to host your data in the cloud. Then, using built-in data labeling functionality, we can apply classification, object detection, and entity extraction, etc., for images, videos, audio, and text. 

Build & Deploy

We can build your machine learning applications on Google Cloud Platform that provides fully configured environments for different machine learning frameworks.
 
Additionally, we can use Google's AI Platform Training and Prediction services to train your models and deploy them to production on GCP as a serverless environment — or do so on-premises (on-prem) using the training and prediction microservices provided by Canonical's Kubeflow.

Manage

Once launched, Google Cloud Platform makes it easy to manage the models, experiments, and end-to-end workflows that we develop for you.

TAKE THE NEXT STEP.

GET STARTED TODAY. 

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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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