New $10 million AWS Artificial Intelligence and
Machine Learning Scholarship (AWS AI & ML Scholarship) program
is designed to prepare underrepresented and underserved students
globally for careers in machine learning
Amazon SageMaker Studio Lab makes it easy for
anyone to quickly set up a machine learning development environment
for learning and experimentation at no cost
Today, at AWS re:Invent, Amazon Web Services, Inc. (AWS), an
Amazon.com, Inc. company (NASDAQ: AMZN), announced two new
initiatives designed to make machine learning more accessible for
anyone interested in learning and experimenting with the
technology. The AWS AI & ML Scholarship is a new education and
scholarship program aimed at preparing underrepresented and
underserved students globally for careers in machine learning. The
program uses AWS DeepRacer and the new AWS DeepRacer Student League
to teach students foundational machine learning concepts by giving
them hands-on experience training machine learning models for
autonomous race cars, while providing educational content centered
on machine learning fundamentals. AWS is further increasing access
to machine learning through Amazon SageMaker Studio Lab, which
gives everyone access to a no-cost version of Amazon SageMaker—an
AWS service that helps customers build, train, and deploy machine
learning models.
“The two initiatives we are announcing today are designed to
open up educational opportunities in machine learning to make it
more widely accessible to anyone who is interested in the
technology,” said Swami Sivasubramanian, Vice President of Amazon
Machine Learning at AWS. “Machine learning will be one of the most
transformational technologies of this generation. If we are going
to unlock the full potential of this technology to tackle some of
the world’s most challenging problems, we need the best minds
entering the field from all backgrounds and walks of life. We want
to inspire and excite a diverse future workforce through this new
scholarship program and break down the cost barriers that prevent
many from getting started with machine learning.”
New $10 million education and scholarship program is designed
to prepare underrepresented and underserved students globally for
careers in machine learning
The World Economic Forum estimates that technological advances
and automation will create 97 million new technology jobs by 2025,
including in the field of artificial intelligence and machine
learning. While the job opportunities in technology are growing,
diversity is lagging behind in science and technology careers.
Making educational resources available to anyone interested in
technology is critical to encouraging a more robust, diverse
pipeline of people in artificial intelligence and machine learning
careers. The new AWS AI & ML Scholarship aims to help
underrepresented and underserved high school and college students
learn foundational machine learning concepts and prepare them for
careers in artificial intelligence and machine learning. In
addition to no-cost access to dozens of hours of free machine
learning model training and educational materials, 2,000 qualifying
students from underrepresented and underserved communities will win
a scholarship for the AI Programming with Python Udacity Nanodegree
program, designed to give scholarship recipients the programming
tools and techniques fundamental to machine learning. Graduates
from the first Nanodegree program will be invited to take a
technical assessment. Five hundred students who receive the highest
scores in this assessment will earn a second Udacity Nanodegree
program scholarship on deep learning and machine learning
engineering to help further prepare them for a career in artificial
intelligence and machine learning. These top 500 students will also
have access to mentorship opportunities from tenured Amazon and
Intel technology experts for career insights and advice.
Delivered in collaboration with Intel and supported by the
talent transformation platform Udacity, the AWS AI & ML
Scholarship program allows students from around the world to access
dozens of hours of free training modules and tutorials on the
basics of machine learning and its real-world applications.
Students can use AWS DeepRacer to turn theory into hands-on action
by learning how to train machine learning models to power a virtual
race car. Students who successfully complete educational modules by
passing knowledge-check quizzes, meet certain AWS DeepRacer lap
time performance targets, and submit an essay will be considered
for Udacity Nanodegree program scholarships. Students can also put
their virtual race cars to the test in the new AWS DeepRacer
Student League. The AWS DeepRacer Student League helps people of
all skill levels learn how to build machine learning models with a
fully autonomous 1/18th scale race car driven by machine learning,
a 3D racing simulator, and a global competition. AWS DeepRacer has
been used by enterprises like Capital One, BMW, Deloitte, JP Morgan
Chase, Accenture, and Liberty Mutual to teach their employees to
build, train, and deploy machine learning models in a hands-on way.
To get started with the AWS AI & ML Scholarship, visit
awsaimlscholarship.com.
Amazon SageMaker Studio Lab provides no-cost access to a
machine learning development environment to put machine learning in
the hands of everyone
Amazon SageMaker Studio Lab offers a free version of Amazon
SageMaker, which is used by researchers and data scientists
worldwide to build, train, and deploy machine learning models
quickly. Amazon SageMaker Studio Lab removes the need to have an
AWS account or provide billing details to get up and running with
machine learning on AWS. Users simply sign up with an email address
through a web browser, and Amazon SageMaker Studio Lab provides
access to a machine learning development environment. Amazon
SageMaker Studio Lab provides unlimited user sessions that include
15 gigabytes of persistent storage to store projects and up to 12
hours of CPU and four hours of GPU compute for training machine
learning models at no cost. There are no cloud resources to build,
scale, or manage with Amazon SageMaker Studio Lab, so users can
start, stop, and restart working on machine learning projects as
easily as closing and opening a laptop. When users are done
experimenting and want to take their ideas to production, they can
easily export their machine learning projects to Amazon SageMaker
Studio to deploy and scale their models on AWS. Amazon SageMaker
Studio Lab can be used as a no-cost learning environment for
students or a no-cost prototyping environment for data scientists
where everyone can quickly and easily start building and training
machine learning models with no financial obligation or long-term
commitments. To learn more about Amazon SageMaker Studio Lab, visit
aws.amazon.com/sagemaker/studio-lab.
Earlier this year, Amazon announced a new Leadership Principle:
Success and Scale Bring Broad Responsibility. AWS is scaling and
investing in initiatives to live up to this new Leadership
Principle, including Amazon's commitment to provide 29 million
people with access to free cloud computing skills training by 2025,
science, technology, engineering, and math (STEM) education
programs for young learners including Amazon Future Engineer, AWS
Girls’ Tech Day, and AWS GetIT, as well as collaborations with
colleges and universities. Now, AWS is making it easier for more
people from underrepresented groups and underserved populations to
get started with machine learning—with free education,
scholarships, and access to the same machine learning technology
used by the world’s leading startups, research institutions, and
enterprises. The two initiatives announced today further advance
Amazon’s efforts to make education and training opportunities
widely accessible.
AWS and Intel have a 15-year relationship dedicated to
developing, building, and supporting cloud services that are
designed to manage cost and complexity, accelerate business
outcomes, and scale to meet current and future computing
requirements. “As an industry, we must do more to create a diverse
and inclusive tech workforce,” said Michelle Johnston Holthaus,
Executive Vice President and GM of the Sales, Marketing, and
Communications Group at Intel. “Intel is proud to support
initiatives like the AWS AI & ML Scholarship program, which
aligns with our commitment to provide more access to STEM
opportunities for underrepresented groups and helps diversify the
future generation of machine learning practitioners. What makes
this education and scholarship program unique is that students are
given access to a rich set of learning materials at the outset.
This is critical to really move the needle. Learning isn’t
contingent on winning but instead part of the process.”
Girls in Tech is a global nonprofit organization dedicated to
eliminating the gender gap in tech. “Driving diversity in machine
learning requires intentional programs that create opportunities
and break down barriers like the new AWS AI & ML Scholarship
program,” said Adriana Gascoigne, Founder and CEO of Girls in Tech.
“Progress in bringing more women and underrepresented communities
into the field of machine learning will only be achieved if
everyone works together to close the diversity gap. Girls in Tech
is glad to see multi-faceted programs like the AWS AI & ML
Scholarship to help close the gap in machine learning education and
open career potential among these groups.”
Hugging Face is an AI community for building, training, and
deploying state of the art models powered by the reference open
source in machine learning. “At Hugging Face, our mission is to
democratize state of the art machine learning,” said Jeff Boudier,
Director of Product Marketing at Hugging Face. “With Amazon
SageMaker Studio Lab, AWS is doing just that by enabling anyone to
learn and experiment with ML through a web browser, without the
need for a high-powered PC or a credit card to get started. This
makes ML more accessible and easier to share with the community. We
are excited to be part of this launch and contribute Hugging Face
transformers examples and resources to make ML even more
accessible!”
Santa Clara University’s mission with the Department of Finance
is to educate students, at the undergraduate and graduate levels,
to serve their organizations and society in the Jesuit tradition.
“Amazon SageMaker Studio Lab will help my students learn the
building blocks of machine learning by removing the cloud
configuration steps required to get started. Now, in my natural
language processing classes, students have more time to enhance
their skills,” said Sanjiv Das, Professor of Finance and Data
Science at Santa Clara University. “Amazon SageMaker Studio Lab
enables students to onboard to AWS quickly, work and experiment for
a few hours, and easily pick up where they left off. Amazon
SageMaker Studio Lab brings the ease of use of Jupyter notebooks in
the cloud to both beginner and advanced students studying machine
learning.”
University of Pennsylvania Engineering is the birthplace of the
modern computer. It was there that ENIAC, the world’s first
electronic, large-scale, general-purpose digital computer, was
developed in 1946. For over 70 years, the field of computer science
at Penn has been marked by exciting innovations. “One of the
hardest parts about programming with machine learning is
configuring the environment to build. Students usually have to
choose the compute instances, security polices, and provide a
credit card,” said Dan Roth, Professor of Computer and Information
Science at University of Pennsylvania. “My students needed Amazon
SageMaker Studio Lab to abstract away all of the complexity of
setup and provide a free powerful sandbox to experiment. This lets
them write code immediately without needing to spend time
configuring the ML environment.”
About Amazon Web Services
For over 15 years, Amazon Web Services has been the world’s most
comprehensive and broadly adopted cloud offering. AWS has been
continually expanding its services to support virtually any cloud
workload, and it now has more than 200 fully featured services for
compute, storage, databases, networking, analytics, machine
learning and artificial intelligence (AI), Internet of Things
(IoT), mobile, security, hybrid, virtual and augmented reality (VR
and AR), media, and application development, deployment, and
management from 81 Availability Zones (AZs) within 25 geographic
regions, with announced plans for 27 more Availability Zones and
nine more AWS Regions in Australia, Canada, India, Indonesia,
Israel, New Zealand, Spain, and Switzerland, and the United Arab
Emirates. Millions of customers—including the fastest-growing
startups, largest enterprises, and leading government
agencies—trust AWS to power their infrastructure, become more
agile, and lower costs. To learn more about AWS, visit
aws.amazon.com.
About Amazon
Amazon is guided by four principles: customer obsession rather
than competitor focus, passion for invention, commitment to
operational excellence, and long-term thinking. Amazon strives to
be Earth’s Most Customer-Centric Company, Earth’s Best Employer,
and Earth’s Safest Place to Work. Customer reviews, 1-Click
shopping, personalized recommendations, Prime, Fulfillment by
Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire
tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology,
Amazon Studios, and The Climate Pledge are some of the things
pioneered by Amazon. For more information, visit amazon.com/about
and follow @AmazonNews.
View source
version on businesswire.com: https://www.businesswire.com/news/home/20211201005992/en/
Amazon.com, Inc. Media Hotline Amazon-pr@amazon.com
www.amazon.com/pr
Amazon.com (NASDAQ:AMZN)
Historical Stock Chart
From Aug 2024 to Sep 2024
Amazon.com (NASDAQ:AMZN)
Historical Stock Chart
From Sep 2023 to Sep 2024