Putting the I in AI
Increasing at a compound annual growth rate of 42%, the global machine learning (ML) industry, will be worth a phenomenal $9 billion towards the end of 2022. Despite this figure, there are many cases of failure to deploy a reliable artificial intelligence (AI) product or service. Part of the failings and shortcomings of AI concern the ramp or process for getting there. For an AI project to succeed, it is critical to have your data fully deployed, available, structured, and cleaned. You also must have some algorithms (machine learning/deep learning/natural language processing) ready that will pull the insights, intelligence, and focused data to put the intelligence into AI.
The promise of machine learning
The, as yet untapped, real power of machine learning to fuel AI applications and services and make them truly intelligent is immense. This expectation is continually shifting. However, with so many different technology fields colliding, it is a difficult area to master. Deep reinforcement learning (DRL), natural language processing (NLP), AutoML tools, ML Ops, neural networks, and model-based reinforcement learning are just some of the subjects required to master machine learning.
Think of the enormous potential if your organization, or at the very least someone in your organization, fully understands machine learning. I’m talking about finding anomalies before they ship or break your manufacturing process or imagine knowing what your customers want before they express it? Apart from the commercial capability, there is also how we can use this information to, for example, avert traffic or prevent accidents thanks to useful inputs into navigation.
The vital role machine learning architects play in business
As tech professionals master deep learning and drive business innovation and improvements, we will see more specialized tooling and infrastructure. In turn, this will mean an increase in the usage of cloud technologies for customized use cases.
For example, at Skillsoft I’d like to see us utilize ML to merge multi-modal sensory inputs/outputs (sound, touch, and vision for watch, read, listen, practice) for human interaction. Learners of the future will have learning containers constructed to adapt to their needs, requirements, capabilities, and preferences without having to express anything explicitly.
Role-Based Path: ML Programmer to Machine Learning Architect
How does an organization find the right people to build their applications and services of the future? Or better yet, how do they upskill, reskill, or preskill their existing talent to perform the roles needed to implement AI & ML fully? Skillsoft believes if you provide prescriptive and progressive curricula around the essential topics that a budding Deep Learning Programmer needs, you can equip them with the skills and knowledge to make a difference and move them forward in their organization.
As you can see, this Skillsoft Aspire journey from Machine Learning Programmer to Machine Learning Architect takes numerous twists and turns. Why? When thinking about how you can train the prototypical ML expert of the future, you must consider all the deep tech skills needed. You also want the business and social skills that are required to attain the expert moniker. A well-rounded ML Architect will see the business imperatives, understand how to communicate with engineering and business, and have the tech skills to deploy ML models that benefit your business and customers.
Take the first steps on this path today and see for yourself where Skillsoft Aspire learning journeys can take you.
Mike Hendrickson is the VP, Technology & Developer Products at Skillsoft.