The new $10 million AWS Artificial Intelligence and Machine Learning Scholarship (AWS AI and ML Scholarship) program aims to prepare underrepresented and underserved students worldwide for a career in machine learning

Amazon SageMaker Studio Lab makes it easy and fast for anyone to set up a machine learning development environment to learn and experiment for free

LAS VEGAS--(BUSINESS WIRE)--(BUSINESS WIRE)--Today, on AWS re:Invent, Amazon Web Services, Inc. (AWS) (NASDAQ (Stock code: AMZN) announced two new plans aimed at making machines. Anyone interested in learning and experimenting with the technology can learn more easily. The AWS AI and ML Scholarship is a new education and scholarship program designed to prepare underrepresented and underserved students worldwide for a career in machine learning. The program uses AWS DeepRacer and the new AWS DeepRacer Student Alliance to teach students basic machine learning by providing students with hands-on experience in training machine learning models for automatic racing, while providing educational content centered on the basics of machine learning concept. AWS is further increasing access to machine learning through the Amazon SageMaker Studio Lab, which allows everyone to access the free version of Amazon SageMaker, an AWS service that helps customers build, train, and deploy machine learning models.

Swami Sivasubramanian, vice president of machine learning at AWS, said: “The two initiatives we announced today are aimed at providing educational opportunities for machine learning so that anyone interested in the technology can use it more widely.” “Machine learning will become a reality. One of the most transformative technologies of this generation. If we want to release the full potential of this technology to solve some of the most challenging problems in the world, we need the best talents from all backgrounds and all walks of life to enter the field We hope that through this new scholarship program, we will inspire and inspire a diversified workforce in the future, and break down the cost barriers that prevent many people from starting to use machine learning."

The new $10 million education and scholarship program aims to prepare underrepresented and underserved students worldwide for a career in machine learning

The World Economic Forum estimates that by 2025, technological progress and automation will create 97 million new technology jobs, including artificial intelligence and machine learning. Although job opportunities in the technical field are increasing, the diversity of technical careers has fallen behind. Providing educational resources to anyone interested in technology is essential to encourage stronger and more diverse talent pipelines in the AI ​​and machine learning professions. The new AWS AI and ML scholarships are designed to help underrepresented and underserved high school and college students learn basic machine learning concepts and prepare them for careers in artificial intelligence and machine learning. In addition to free dozens of hours of free machine learning model training and educational materials, 2,000 eligible students from underrepresented and underserved communities will receive scholarships for the Python Artificial Intelligence Programming Udacity Nanodegree Program, which aims to provide Scholarship recipients provide basic tools and techniques for programming machine learning. Graduates of the first nanodegree program will be invited to participate in a technology assessment. The 500 students with the highest scores in this assessment will receive deep learning and machine learning engineering scholarships for the second Udacity nanodegree program to help them further prepare for careers in artificial intelligence and machine learning. These top 500 students will also receive mentoring opportunities from Amazon and Intel technical experts to gain career insights and advice.

The AWS Artificial Intelligence and Machine Learning Scholarship Program, provided in collaboration with Intel and supported by the talent transformation platform Udacity, allows students from all over the world to access dozens of hours of free training modules and tutorials on the basics of machine learning and its practical applications. Students. You can use AWS DeepRacer to learn how to train machine learning models to power virtual racing cars, thereby transforming theory into actual operations. Students who successfully complete the education module through the knowledge check test, reach the specific AWS DeepRacer single-lap performance goal and submit a thesis will be considered for the Udacity Nanodegree Program Scholarship. Students can also test their virtual racing cars in the new AWS DeepRacer Student League. The AWS DeepRacer Student Alliance helps people of all skill levels learn how to build machine learning models using fully autonomous 1/18 scale racing cars driven by machine learning, 3D racing simulators, and global competitions. Companies such as Capital One, BMW, Deloitte, JPMorgan Chase, Accenture, and Liberty Mutual have used AWS DeepRacer to teach their employees to build, train, and deploy machine learning models in a hands-on manner. To start using AWS AI and ML scholarships, please visit awsaimlscholarship.com.

Amazon SageMaker Studio Lab provides free access to the machine learning development environment, so everyone can master machine learning

Amazon SageMaker Studio Lab provides a free version of Amazon SageMaker, which is used by researchers and data scientists all over the world to quickly build, train, and deploy machine learning models. Amazon SageMaker Studio Lab does not need to have an AWS account or provide billing details to start and run machine learning on AWS. Users only need to register with an email address through a web browser, and the Amazon SageMaker Studio laboratory provides access to the machine learning development environment. Amazon SageMaker Studio Lab provides unlimited user sessions, including 15 GB of persistent storage to store items, and up to 12 hours of CPU and 4 hours of GPU computing for free training of machine learning models. There is no need to build, expand, or manage cloud resources with Amazon SageMaker Studio Lab, so users can start, stop, and restart machine learning projects as easily as closing and opening a laptop. When users complete the experiment and want to put their ideas into practice, they can easily export their machine learning projects to Amazon SageMaker Studio to deploy and extend their models on AWS. Amazon SageMaker Studio Lab can be used as a free learning environment for students or a free prototyping environment for data scientists, where everyone can quickly and easily start building and training machine learning models without any financial obligations or long-term commitments. To learn more about Amazon SageMaker Studio Lab, please visit aws.amazon.com/sagemaker/studio-lab.

Earlier this year, Amazon announced a new leadership principle: Success and scale bring broad responsibilities. AWS is expanding and investing in initiatives to achieve this new leadership principle, including Amazon’s commitment to provide 29 million people with free cloud computing skills training by 2025, and science, technology, engineering, and mathematics (STEM) education programs for young people Learners include 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 the disadvantaged and underserved to start machine learning-providing free education, scholarships, and using the same machine learning technology used by the world's leading startups, research institutions, and enterprises. The two initiatives announced today further boost Amazon’s efforts to provide a wide range of education and training opportunities.

AWS and Intel have a 15-year partnership and are committed to developing, building, and supporting cloud services designed to manage cost and complexity, accelerate business results, and scale to meet current and future computing needs. "As an industry, we must take more measures to build a diverse and inclusive technical workforce," said Michelle Johnston Holthaus, executive vice president and general manager of Intel's sales, marketing and communications business unit. "Intel is proud to support programs such as the AWS AI and ML scholarship programs, which is in line with our commitment to provide more STEM opportunities for underrepresented groups and will help diversify future generations of machine learning practitioners. This education The uniqueness of the and scholarship program is that students can get a wealth of learning materials from the beginning. This is essential for truly moving the pointer. Learning does not depend on winning, but is part of the process."

Girls in Tech is a global non-profit organization dedicated to bridging the gender gap in technology. "Promoting the diversity of machine learning requires conscious programs that can create opportunities and break down barriers, such as the new AWS AI and ML scholarship programs," said Adriana Gascoigne, founder and CEO of Girls in Tech. "Only when everyone works together to close the diversity gap can we make progress in bringing more women and underrepresented communities into the field of machine learning. Girls in Tech is pleased to see the AWS AI and ML scholarships and other multi-faceted programs to help Close the gap between these groups in machine learning education and unlock their career potential."

Hugging Face is an AI community for building, training, and deploying state-of-the-art models powered by reference open source in machine learning. "At Hugging Face, our mission is to democratize the most advanced machine learning," said Jeff Boudier, director of product marketing at Hugging Face. "With Amazon SageMaker Studio Lab, AWS has done this, allowing anyone to learn and experiment with ML through a web browser without needing a high-performance PC or credit card to get started. This makes ML more accessible and easier to communicate with the community Share. We are excited to participate in this release and contribute Hugging Face converter examples and resources to make ML more accessible!"

The mission of Santa Clara University and the Department of Finance is to educate students at the undergraduate and graduate level, and serve their organization 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 course, students have more time to improve their skills," Santa Clara Said Sanjiv Das, professor of finance and data science at the University of Latin America. "Amazon SageMaker Studio Lab enables students to quickly join AWS, conduct hours of work and experimentation, and then easily continue learning from where they left off. Amazon SageMaker Studio Lab brings the cloud to beginners and advanced students learning machine learning The ease of use of Jupyter notebooks."

The School of Engineering at the University of Pennsylvania is the birthplace of modern computers. In 1946, ENIAC, the world's first electronic, large-scale, general-purpose digital computer was developed there. For more than 70 years, the computer science field at the University of Pennsylvania has been marked by exciting innovations. "One of the most difficult parts of programming with machine learning is configuring the environment to be built. Students usually have to choose a computing instance, a security policy, and provide a credit card," said Dan Roth, a professor of computer and information science at the University of Pennsylvania. "My students need Amazon SageMaker Studio Lab to eliminate all the complexity of setup and provide a free powerful sandbox for experimentation. This allows them to write code immediately without having to spend time configuring the ML environment."

For 15 years, Amazon Web Services has been the most comprehensive and widely adopted cloud product in the world. AWS has been continuously expanding its services to support almost all cloud workloads, and now it has more than 200 full-featured services for computing, storage, database, networking, analytics, machine learning and artificial intelligence (AI), the 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 (AZ) in 25 geographic regions, and announced plans to increase 27 AWS regions in Australia, Canada, India, Indonesia, Israel, New Zealand, Spain, Switzerland, and the United Arab Emirates. Millions of customers—including the fastest-growing startups, the largest enterprises, and leading government agencies—trust AWS to support their infrastructure, become more agile and reduce costs. To learn more about AWS, please visit aws.amazon.com.

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AWS announced two new plans to make machine learning more accessible.

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