Ds Scholarship

Of The People, For The People, By The Machine

Technology is a democratic right. This is not a legal statement, a truism, or even any kind of de facto public awareness declaration. It’s just something we all tend to agree on. The birth of cloud computing and the emergence of open source has fueled this line of thinking, that is, the cloud puts access and power in anyone’s hands and places the merit of the open source champions on the hierarchy, an action in itself that insists on access, opportunity, and participation.

Among the sectors of the IT landscape that are now being pushed towards a more democratic level of access are artificial intelligence (AI) and machine learning (ML) methods that aim to build ‘intelligence’ within AI models and their algorithmic powerhouse.

Amazon Web Services (AWS) clearly plays a major role in the cloud, and as such has the ability to bring the power of data center ML forward in different ways, in different formats and at different levels of complexity, abstraction, and ease of use.

While some democratization of IT focuses on putting complex developer tools and data science in the hands of ordinary people, other drives of democratization to put machine learning tools in the hands of developers… Not all of them will be natural machine learning specialists and AI engineers in the first place. .

Hands-on AI and machine learning experiments

The recently announced SageMaker Studio Lab is a free service for software application developers to learn machine learning methods. It teaches them basic techniques and provides them with the opportunity to run hands-on experiments with an integrated development environment (in this case, the JupyterLab IDE) to start creating typical training functions that will run on real-world processors (both CPU chips and high-end GPUs, or GPUs) as well as Gigabytes of storage required by these operations as well.

AWS has twinned its product development by creating its own AWS AI & ML Scholarship Program. This is an investment of $10 million annually in a learning and mentorship initiative created in collaboration with Intel and Udacity.

“Machine learning is going to be one of the most transformative technologies of this generation. If we are to unlock the full potential of this technology to tackle some of the world’s most challenging problems, we need the best minds coming into the field from all backgrounds and walks of life,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning At AWS, we want to inspire and excite a diverse workforce of the future with this new scholarship program and break down the cost barriers that prevent many from getting started.

Girls in Tech founder and CEO Adriana Gascoigne wholeheartedly agrees with the message of diversity sent by Sivasubramanian. Her organization is a global nonprofit dedicated to closing the gender gap in technology and she welcomes what she calls “intent programs” like these designed to break down barriers.

“Progress in bringing more women and underrepresented communities into the field of machine learning will only come if everyone works together to close the diversity gap. Girls in Tech are excited to see multifaceted programs like the AWS AI & ML Scholarship to help bridge the gap in machine learning and unlock career potential.” Among these groups,” Gascoigne said.

The program uses the AWS DeepRacer (an integrated learning system for users of all levels to learn and explore reinforcement learning and to experiment and build autonomous driving applications) and the new AWS DeepRacer Student League to teach students foundational machine learning concepts by giving them hands-on experience with machine learning models for training in self-driving race cars, While providing educational content focused on the basics of machine learning.

The World Economic Forum estimates that technological advances and automation will create 97 million new technical jobs by 2025, including in artificial intelligence and machine learning. While job opportunities in technology are increasing, diversity in science and technology jobs lags behind.

The birthplace of the modern computer

Many in the tech field consider the University of Pennsylvania Engineering to be the birthplace of the modern computer. This honor and reputation is due to the fact that ENIAC, the world’s first general-purpose large-scale electronic digital computer, was developed there in 1946. University Professor of Computer and Information Sciences (CIS) Dan Roth is passionate about the topic of democratizing artificial intelligence and machine learning.

“One of the hardest parts of programming with ‘machine learning’ is setting up the environment to build. Students usually have to choose account instances, security policies, and provide a credit card,” Roth said. Powerful protection and free to try. This allows them to write code on the spot without having to spend time configuring the ML environment.”

In terms of how these systems and initiatives actually work, Amazon SageMaker Studio Lab offers a free version of Amazon SageMaker, which researchers and data scientists around the world use to quickly build, train, and deploy machine learning models.

Amazon SageMaker Studio Lab eliminates the need to have an AWS account or provide billing details to get machine learning up and running 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.

Machine learning without code

This thread of industry effort should also logically include the use of low-code/no-code (LC/NC) technologies. AWS has included this element in its platform with what it calls Amazon SageMaker Canvas. This is a no-token service that aims to extend access to machine learning to “business analysts” (a term AWS uses to identify broadly line-of-business employees who support finance, marketing, operations, and HR teams) with a visual interface that allows them to create accurate machine learning predictions themselves, without Need to write one line of code.

Amazon SageMaker Canvas provides a visual user interface with a click and point for users to create predictions. Customers direct Amazon SageMaker Canvas to their own data stores (eg Amazon Redshift, Amazon S3, Snowflake, local data stores, local files, etc.) and Amazon SageMaker Canvas provides visual tools to help users prepare and analyze data intuitively.

Amazon SageMaker Canvas uses machine learning to build and train machine learning models without any coding. Business people can review and rate models in the Amazon SageMaker Canvas console for accuracy and effectiveness in a use case. Amazon SageMaker Canvas also allows users to export their models to Amazon SageMaker Studio, so they can share them with data scientists to validate and improve their models.

According to Mark Neumann, Product Owner, AI Platform at BMW Group, the use of AI as a core technology is an essential component of the BMW Group’s digital transformation process. The company already employs AI throughout its value chain, but it is scaling when using it.

“We believe Amazon SageMaker Canvas can add a boost to scaling our AI/ML across the BMW family. With SageMaker Canvas, our business users can easily explore and build ML models to make accurate predictions without writing any code. SageMaker also allows our central data science team to We have to collaborate and evaluate models created by business users before they are put into production,” Neumann said.

With great strength comes great responsibility

As we know, with all the great power comes great responsibility and there is nowhere healthier than it is in the world of artificial intelligence and machine learning with all the power of the machine brain we are about to use in our lives.

Organizations can, of course, collect, contain, and control the amount of ML an individual, team, or department can access – and the internal and external systems they can connect to and influence – via policy controls and role-based access systems that ensure data is not tampered with and subsequently distributed in ways that may prove In the end it is harmful to business, or indeed to people.

There is no denying the general weight of the efforts here as AI and machine learning realization are democratized for the sake of a larger segment of society… After all, who wouldn’t vote for that?

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