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Technology is a democratic right. Thats not a legal statement, a core truism or even any kind of de facto public awareness proclamation. Its just something that we all tend to agree upon. The birth of cloud computing and the rise of open source have fuelled this line of thought i.e. cloud puts access and power in anyones hands and open source champions meritocracy over hierarchy, an action which in itself insists upon access, opportunity and engagement.

Key among the sectors of the IT landscape now being driven towards a more democratic level of access are Artificial Intelligence (AI) and the Machine Learning (ML) methods that go towards building the smartness inside AI models and their algorithmic strength.

Amazon Web Services (AWS) is clearly a major player in cloud and therefore has the breadth to bring its datacenters ML muscle forwards in different ways, in different formats and at different levels of complexity, abstraction and usability.

While some IT democratization focuses on putting complex developer and data science tools in the hands of laypeople, other democratization drives to put ML tools in the hands of developers not all of whom will be natural ML specialists and AI engineers in the first instance.

The recently announced SageMaker Studio Lab is a free service for software application developers to learn machine learning methods. It teaches them core techniques and offers them the chance to perform hands-on experimentation with an Integrated Development Environment (in this case, a JupyterLab IDE) to start creating model training functions that will work on real world processors (both CPU chips and higher end Graphic Processing Units, or GPUs) as well as the gigabytes of storage these processes also require.

AWS has twinned its product development with the creation of its own AWS AI & ML Scholarship Program. This is a US$10 million investment per year learning and mentorship initiative created in collaboration with Intel and Udacity.

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 worlds 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, said Swami Sivasubramanian, VP of Amazon Machine Learning at AWS.

Founder and CEO of Girls in Tech Adriana Gascoigne agrees with Sivasubramanians diversity message wholeheartedly. Her organization is a global nonprofit dedicated to eliminating the gender gap in tech and she welcomes what she calls intentional programs like these that are designed to break down barriers.

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, said Gascoigne.

The program uses 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 training machine learning models for autonomous race cars, while providing educational content centered on machine learning fundamentals.

The World Economic Forum estimates that technological advances and automation will create 97 million new technology jobs by 2025, including in the field of AI & ML. While the job opportunities in technology are growing, diversity is lagging behind in science and technology careers.

The University of Pennsylvania Engineering is regarded by many in technology as the birthplace of the modern computer. This honor and epithet is due to the fact that ENIAC, the worlds first electronic, large-scale, general-purpose digital computer, was developed there in 1946. Professor of Computer and Information Science (CIS) at the university Dan Roth is enthusiastic on the subject of AI & ML democratization.

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 Roth. 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.

In terms of how these systems and initiatives actually work, 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.

This thread of industry effort must also logically embrace the use of Low-Code/No-Code (LC/NC) technologies. AWS has built this element into its platform with what it calls Amazon SageMaker Canvas. This is a No-Code service intended to expands access to Machine Learning to business analysts (a term that AWS uses to broadly define line-of-business employees supporting finance, marketing, operations and human resources teams) with a visual interface that allows them to create accurate Machine Learning predictions on their own, without having to write a single line of code.

Amazon SageMaker Canvas provides a visual, point-and-click user interface for users to generate predictions. Customers point Amazon SageMaker Canvas to their data stores (e.g. Amazon Redshift, Amazon S3, Snowflake, on-premises data stores, local files, etc.) and the Amazon SageMaker Canvas provides visual tools to help users intuitively prepare and analyze data.

Amazon SageMaker Canvas uses automated Machine Learning to build and train machine learning models without any coding. Businesspeople can review and evaluate models in the Amazon SageMaker Canvas console for accuracy and efficacy for their use case. Amazon SageMaker Canvas also lets users export their models to Amazon SageMaker Studio, so they can share them with data scientists to validate and further refine their models.

According to Marc Neumann, product owner, AI Platform at The BMW Group, the use of AI as a key technology is an integral element in the process of digital transformation at the BMW Group. The company already employs AI throughout its value chain, but has been working to expand upon its use.

We believe Amazon SageMaker Canvas can add a boost to our AI/ML scaling across the BMW Group. 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 collaborate and evaluate the models created by business users before publishing them to production, said Neumann.

As we know, with all great power comes great responsibility and nowhere is this more true than in the realm of AI & ML with all the machine brain power we are about to wield upon our lives.

Enterprises can of course corral, contain and control how much ML any individual, team or department has access to - and which internal and external systems it can then further connect with and impact - via policy controls and role-based access systems that make sure data sources are not manipulated and then subsequently distributed in ways that could ultimately prove harmful to the business, or indeed to people.

There is no denying the general weight of effort being applied here as AI intelligence and ML cognizance is being democratized for a greater transept of society and after all who wouldnt vote for that?

See the original post here:
Machine Learning Democratized: Of The People, For The People, By The Machine - Forbes