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Now Accepting Applications. Earn Your Google Cloud Architect Certification for Free.

Portland, OR — Manceps, the artificial intelligence research and engineering firm, announced Monday that they are now accepting applications for their 2020 Scholarship. They year, they are giving data scientists and AI Professionals the chance to earn their Google Cloud Architect Certification for free. The prestigious certification was ranked the highest paying IT credential of 2019.

Here’s how it works: simply apply for the scholarship by clicking the link below, prepare for your exam, pass your test, and we’ll reimburse you your test registration fees. As the economy continues to struggle, now is the perfect time to add a well-known and highly regarded certification to your resume -- all for free. Spots are limited.

About the Google Cloud Architect Certification

A Professional Cloud Architect enables organizations to leverage Google Cloud technologies. With a thorough understanding of cloud architecture and Google Cloud Platform, this individual can design, develop, and manage robust, secure, scalable, highly available, and dynamic solutions to drive business objectives.

The Google Cloud Certified Professional Cloud Architect exam assesses your ability to:

• Design and plan a cloud solution architecture
• Manage and provision the cloud solution infrastructure
• Design for security and compliance
• Analyze and optimize technical and business processes
• Manage implementations of cloud architecture
• Ensure solution and operations reliability

Click here to apply. →

13.04.2020

The Complete Guide to Bringing AI to Your Organization

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Our Most Recent Articles

Video: Machine Learning Engineering with Tensorflow Extended

Video: How to Build a Reproducible ML Pipeline

Video: ML adventures with AutoML and TFHub

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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