The AI skills gap is one of the biggest barriers keeping AI from entering the mainstream as a viable business-enabling and problem-solving technology. With an estimated 700,000 job openings for data scientists in the U.S. alone expected by 2020 this skills gap is acute. And we think the problem will only worsen. As AI-based projects become more prevalent, demand will only increase.
Reducing the skills gap in deep learning
Without a change in trajectory, the competition for data science talent is going to significantly handicap organizations, especially smaller organizations without deep pockets. In the deep learning space, where technology and the underlying domain are even more complex, the talent issue will be even more acute.
Until now, most conversations around a solution have been primarily about increasing data science educational opportunities. Master’s and Ph.D.s in computer science and mathematics plus more courses focusing in on “data science” are a route to fill the gaps. The huge increase in more practical and applied courses linked to machine learning and deep learning from online open courses and/or top technology schools and other providers show the education market is responding.
But despite growing access to data science programs and the hunger for data scientists, the disparity between capable data scientists and the need for their talent widens every day. Additional solutions are necessary if AI is to fulfill its promise of changing the world.
Part of the answer is the operational AI platform.
The solution: Operational AI
Operational AI is a new type of software platform designed for a wider audience than elite data scientists. It’s a cloud-based platform with an intuitive graphical interface where the end-to-end workflow of a deep learning project can be run within a single environment without relying on additional resources, tools, software and bespoke coding. Operational AI covers the full workflow, from data preparation to model build to evaluation and deployment.
Additional solutions are necessary if AI is to fulfill its promise of changing the world
An operational AI platform can deliver performant models and then integrate into an organization's external and internal-facing systems to allow a more repeatable and predictable deployment flow. Operational AI platforms offer faster deployment, more data flexibility, heightened usability, audit transparency and collaboration capability. Essentially, an AI platform is both comprehensive and flexible. This, in and of itself, helps address the skills gap with “usable” software that allows a broader group of users to lead AI projects to completion.
With operational AI in place, an organization is no longer 100% reliant on these scarce data scientists for deep learning expertise, but can instead enable others within the data science team to lead projects or go wider in the development organization.
The exponential effect of community enablement
Along with any new technology comes a need for support and community. Any software becomes better when enabled, which is why the great software companies actively enable their users to excel by providing not just a platform, but the resources and means to interaction so that users can better engage with the platform and each other. An enabled community of users creates a deeper understanding of how to use the software, but also how to push the limits of imagination and skill and allow users to do great things with the software.
Operational AI is an exciting field that’s evolving rapidly and constantly
Communities teach users how to use software with more expertise, and inspire them to do great things with that knowledge. Communities build momentum as members learn from each other — both from experts in their midst and from peers working toward similar usage and outcomes. This propels not just the users but the entire platform forward.
02/ More on Business & AI
The AI struggle is real: Thoughts from Web Summit 2019
Getting unstuck: How to adopt a creative mindset around AI challenges
How NLP and BERT will change the language game
Taking the pulse on global AI industry: Almedalen 2019
The value-add of deep learning in predictive maintenance
In a time when manufacturing companies are under intense pressure to improve efficiency and productivity, every gain is valuable
AI is not a silver bullet
Towards operational AI
10 applicable areas for AI: Beyond chatbots and customer recommendations
Machine and deep learning: Non-critical deployment
Machine and deep learning: Experimentation stage
You will never understand AI if you only try to understand AI
In every "AI story," there are five main chapters. You need to start from the end.
Five reasons why you are not data-driven
Peltarion rocks Slush – the leading startup event in Europe
Increasing the deep learning footprint: From niche to mass adoption
How to get your business AI-ready
Shifting the 90/10 factor to elevate AI project productivity
Tallinn Digital Summit 2018 highlights
As part of the Swedish expert delegation at Tallinn Digital Summit, our Anders Arpteg summarizes key takeaways from presentations and discussions with government officials and AI experts from around the world.
Mapping the challenges of deep learning for a strategic approach
The dangers of AI purgatory for any new project
Three takeaways on trust in AI from the Almedalen Week 2018