Artificial Intelligence (AI) is estimated to pave the way for close to 2.3 million opportunities by 2020, according to a Gartner report. With growing volumes and types of data, easy computational processing and data storage, the importance of leveraging machine learning is increasing. It can produce models that can efficiently analyze bigger, more complex data and deliver faster and more accurate results.
This can help organizations build precise models with which they can create opportunities and avoid risks. For instance, it can help them identify any anomalies that may arise in the manufacturing or delivery of their products and empower them with the foresight of their customer needs. Therefore, it is permeating different sectors ranging from healthcare to education, manufacturing to consumer products, and a host of other verticals.
Yet, the potential of ML to fuel artificial intelligence (AI) applications and services still remains untapped as there is a severe shortage of qualified candidates with the skills required to leverage these transformative technologies.
Creating Meaningful AI Applications, Products and Services Requirements
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 infrastructure fully-deployed, with real-time availability, and the data itself is structured, normalized and cleaned. You must have algorithms (machine learning/deep learning/natural language processing) ready that will pull the insights, intelligence, and focused data to put the intelligence into AI.
Let’s look at how to do this.
Creating a Skilled Workforce: Need for Up-Skilling in ML
A machine learning master is the most sought after and in-demand job today. Organizations can fill this gap by developing talent from within, which is the most effective, affordable and efficient way to build a team of machine learning masters.
But in order to master ML, technology professionals must have expertise in deep reinforcement learning (DRL), natural language processing (NLP), Automachine learning tools, machine learning Ops, neural networks, and model-based reinforcement learning. Relying solely on technical knowledge is also not enough to leverage machine learning to its full potential. Professionals also need to possess related business and social skills. This can make a well-rounded machine learning architect, who can look at the business imperatives, engineering and business communication, and skillfully deploy machine learning models.
So how does an organization find the right talent to build artificial intelligence and machine learning-driven applications to steer into the future? The answer lies in getting the right blend of upskilling and reskilling of their existing talent pool.
From Machine Learning Programmer to Machine Learning Architect
Organizations need to first identify the right set of people, for example, those who understand data but need to know machine learning theory. Equipping these employees with a curriculum that’s not only progressive but also prescribes them with the essential topics that a budding ML programmer would need, will help both them and your organization grow.
Here’s a quick snapshot of how an ML programmer can chart his/her career path to eventually transform into a machine learning architect.
From this, businesses and their customers stand to benefit significantly. As technology advances, the amount of development that machine learning skills can unlock is exponential. Since companies seek a skilled workforce that is also familiar with the company culture, developing talent from within is the most effective and affordable way to acquire a team of machine learning masters. This will eventually help organizations succeed at carving ways of simplifying operations further and focus on sustainable growth aspects.