School of Cloud Computing Courses

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All School of Cloud Computing Courses

We found 6 courses available for you
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Nanodegree Cloud DevOps AWS.

419 Lessons
Intermediate

This program is designed to prepare people to become devops …

What you'll learn
This program is designed to prepare people to become devops engineers.
This includes job titles such as DevOps Engineer, Reliability Engineer, Release Manager, and more.
Obtaining the skills required to be a DevOps will make you extremely valuable across many industries, and in many roles.
As a graduate of this program, you’ll be prepared to seek out roles that run the gamut from generalist to specialist, and all points in between.
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Nanodegree Data Engineer

435 Lessons
Intermediate

The data engineering field is expected to continue growing rapidly …

What you'll learn
This program is designed to prepare people to become data engineers. This includes job titles such as analytics engineer, big data engineer, data platform engineer, and others. Data engineering skills are also helpful for adjacent roles, such as data analysts, data scientists, machine learning engineers, or software engineers.
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Google Cloud Certified Professional Machine Learning Engineer (Live)

22 Lessons
Intermediate

Inicia 30 de Octubre.  11 semanas.   Un Professional Machine …

What you'll learn
Aprenda las habilidades necesarias para tener éxito en un puesto de ingeniería de Machine Learning.
Prepárese para el examen de certificación de ingeniero de aprendizaje automático profesional de Google Cloud.
Comprenda cómo diseñar, crear y convertir en producción modelos de AA para resolver los desafíos empresariales mediante las tecnologías de Google Cloud.
Comprender el propósito y la intención de la certificación Professional Machine Learning Engineer y su relación con otras certificaciones de Google Cloud.
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What you'll learn
Test Python code and build a Python package of their own.
Build predictive models using a variety of unsupervised and supervised machine learning techniques.
Use Amazon SageMaker to deploy machine learning models to production environments, such as a
web application or piece of hardware..
A/B test two different deployed models and evaluate their performance.
Utilize an API to deploy a model to a website such that it responds to user input, dynamically.
Update a deployed model, in response to changes in the underlying data source.
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