The future of AI has arrived. From technology and finance to healthcare, retail and manufacturing, nearly every industry today is starting to incorporate artificial intelligence (AI) into their technology platforms and business operations. As a result, the demand for engineers who can design, implement, leverage, and manage AI systems is rapidly increasing.
Over the next decade, the need for AI talent will only increase. The U.S. Bureau of Labor Statistics predicts that the demand for AI engineers will be: 23% increase by 2030 Demand for machine learning (ML) engineers, a subfield of AI, is Grow up to 22%.
This demand is in full swing in the technology industry. Job openings seeking generative AI skills increased by a staggering 1,848% in 2023. According to recent labor market analysis. The analysis also found that in 2023, he had more than 385,000 job openings for AI roles.
Figure 1: Growth in jobs requiring generative AI skills (2022-2023)
To take full advantage of AI's transformative potential, companies can do more than just hire new AI engineers. We still don't have enough human resources. Addressing the global shortage of AI engineering talent requires upskilling and reskilling existing engineers.
Essential AI and ML skills
AI and its subfields machine learning (ML) and natural language processing (NLP) all require algorithms to be trained on large datasets to produce models that can perform complex tasks. As a result, many types of AI engineering roles require many of the same core skills.
code signal talent science team and technical subject matter experts conducted extensive skills mapping of AI engineering roles to define the skills needed for these roles. These are the core skills they identified for two popular AI roles: ML engineering and NLP engineering.
Develop AI skills with your team
a Recent McKinsey Report found that upskilling and reskilling are core ways for organizations to close the AI skills gap within their teams. McKinsey senior partner Alexander Skalevsky explains in the report: Almost half of the companies we surveyed do so. ”
So what's the best way to develop the necessary AI skills within existing teams? To answer that, we first need to dig deeper into how humans learn new skills.
Components of effective skill development
Currently, most corporate learning programs use the traditional model of classroom learning, where one teacher serves many learners in one lesson. Employees begin by choosing a program, often with little guidance. When you start a course, lessons use videos to provide instruction, followed by quizzes to assess your retention of the information.
This model has some problems.
- Decades of research have shown that the traditional one-to-many learning model is not the most effective way to learn.educational psychologist benjamin bloom It was observed that students who learned through one-on-one tutoring performed two standard deviations better than their peers. That is, they outperformed her 98% of students who learned in a traditional classroom environment. The superiority of one-on-one tutoring over classroom learning has been referred to as the two-sigma problem in education (see Figure 2 below).
- Multiple-choice quizzes provide insufficient signals about employee skills, especially for specialized technical skills such as AI and ML engineering. Quizzes also do not give learners the opportunity to apply what they have learned in a realistic context or work flow.
- Without guidance based on their current skills, strengths, goals, and team needs, employees may choose courses or learning programs that don't align with their skill proficiency or goal level.
Figure 2: Comparing the distribution of student grades by teaching style reveals a 2 sigma difference in mean grade scores.
To help your team members acquire the AI and ML skills your team needs, you need a learning program that provides:
- One-on-one instruction. Today’s best-in-class technology learning programs use AI-powered assistants that are context-aware and fully integrated with the learning environment to provide personalized, one-on-one guidance and feedback to learners at scale. Masu.
Using AI to support learning comes as no surprise to developers and other tech employees. recent research According to , 81% of developers are already using AI tools in their work, and 76% of them are using AI tools to learn new knowledge and skills.
- Practice-based learning. Decades of research have shown that People learn best through active practice., rather than passively ingesting information. The learning programs you use to level up your team's skills in AI and ML should be hands-on and utilize coding exercises that simulate real-world AI and ML engineering work.
- Results-oriented tools. Finally, the best technical upskilling programs allow employees to actually build relevant skills (rather than just checking a box) and apply what they learn on the job. Learning programs should also allow managers to understand the skill growth and proficiency of their team members. The platform should include benchmarking data that allows you to compare your team's skills to a larger pool of technical talent. It can also be integrated with existing learning systems.
More information: Practice-based learning for AI skills
Below are examples of advanced exercises. Introduction to Neural Networks with TensorFlow in code signal development.
Practical example: Implementing layers in neural networks
In this exercise, learners build skills in designing neural network layers to improve network performance. Learners implement their solutions in the realistic Her IDE and built-in terminal on the right side of the screen, and interact with Her Cosmo, her AI-powered instructor and guide, in the panel on the left side of the screen.
Exercise description: Now that we have trained the model using additional epochs, let's fine-tune the neural network architecture. Your task is to implement his second dense layer in the neural network to improve its learning capabilities. Remember: Effectively structuring your layers is critical to model performance.
conclusion
The demand for AI and ML engineers is here and will continue to grow in the coming years as AI technologies become critical to more and more organizations across all industries. Companies looking to close the AI and ML skills gap within their teams will need to invest in upskilling and reskilling their existing technical teams with critical AI and ML skills.
