Amazon Web Service Courses

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Cloud Developer Specialization

536 Lessons
Intermediate

3 months program The cloud has become a key enabler …

What you'll learn
This program is designed to prepare students to become Cloud Developers.
This includes job titles such as cloud developer, full stack developer, cloud engineers, and others. Cloud development skills are also helpful for adjacent software engineering roles.

Cloud DevOps AWS Specialization

434 Lessons
Intermediate

3 months program This program is designed to prepare people …

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.

Data Engineer Specialization

433 Lessons
Intermediate

3 months program The data engineering field is expected to …

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.

Machine Learning Engineering AWS Specialization

379 Lessons
Intermediate

3 months program Strengthen your Machine Learning skills and develop …

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.