As customers increasingly adopt artificial intelligence (AI) and machine learning (ML) technologies to gain a competitive edge in the digital economy, the need for cloud expertise is on the rise. The ability to effectively leverage these technologies to drive decision-making and gain insights is becoming critical for success. According to Accenture, “this means both building talent in technical competencies like AI engineering and enterprise architecture, and training people across the organization to work effectively with AI-infused processes." However, the supply of skilled workers in the cloud computing industry remains limited, creating a significant challenge for organizations looking to implement cloud projects. This shortage of skilled workers can lead to increased costs, delays in project implementation, and reduced competitiveness. To address this challenge, customers are investing in training and development programs for their existing employees.
For decades, artificial intelligence (AI) and machine learning (ML) have been instrumental in streamlining processes, enhancing efficiency, and providing valuable insights for organizations. Amazon has been a leader in this field for over 20 years, leveraging ML services from Amazon Web Services (AWS) to power everything from Alexa to Amazon fulfillment center logistics.
AWS customers are transforming their industries with machine learning. AstraZeneca partnered with AWS to utilize Amazon SageMaker, streamlining the process of data analysis and ML model deployment. This collaboration enables AstraZeneca to analyze commercial data efficiently, automate manual processes, and save valuable time for its data scientists. T-Mobile uses AWS machine learning to improve customer service by extracting meaning from customer support tickets and chat transcripts. This helps customer service agents resolve issues more quickly and accurately. The NFL is using AWS machine learning to improve the way they collect, analyze, and use data. This is helping them to better understand the game, make better decisions, and create more engaging experiences for fans.
AI and ML have the potential to revolutionize the way customers operate by automating many tasks currently performed by humans. However, integrating AI and ML workloads necessitates a skilled and diverse team of professionals, emphasizing the need to invest in workforce skilling. The impact on workforces has already begun.
Every job will be impacted by AI. Most of that will be more augmentation rather than replacing workers."
—Pieter den Hamer, Vice president of Research, Gartner
The World Economic Forum's Future of Jobs Report 2023 says that AI and ML specialists, data analysts and scientists, and digital transformation specialists are the most prominent emerging roles. It predicts a 40% jump in the number of AI and machine learning specialists by 2027, a 30-35% rise in demand for roles such as data analysts and scientists or big data specialists, and a 31% increase in demand for information security analysts. This would add a combined 2.6 million jobs.
Introducing AI and ML workloads requires a skilled team of professionals with diverse expertise. Data scientists are needed to identify the relevant data to feed into the AI and ML models and to develop the algorithms that make predictions or recommendations based on that data. Machine learning engineers design and implement the machine learning infrastructure that supports the AI models, while software engineers build and maintain the software systems that run the AI and ML models. Cloud computing specialists and cloud security engineers are necessary to set up and securely manage the cloud infrastructure required to process and store the large amounts of data necessary for AI and ML. Finally, project managers oversee the implementation of AI and ML projects, ensuring they meet business objectives, are delivered on time and within budget, and comply with relevant regulations and ethical considerations. Helping customers introduce AI and ML requires a multidisciplinary team of professionals with expertise in data analysis, machine learning, software engineering, cloud computing, security, privacy, and project management.
AWS helps customers at every stage of their ML adoption journey with the most comprehensive set of AI and ML services, infrastructure, and implementation resources. AWS recently announced Amazon Bedrock, a new service for customers to build and scale generative AI-based applications using FMs, democratizing access for all builders. Also, we are delivering applications like Amazon CodeWhisperer for free, which revolutionizes developer productivity by generating code suggestions in real time. As customers seek to leverage these technologies to automate tasks, improve decision-making, and gain insights from data, they require workers with expertise in cloud-based data analytics, machine learning models, and cloud-based AI platforms. AWS Training and Certification is passionate about helping organizations of all sizes upskill their workforce to capture the full value of AI and ML.
To capture this value, leaders are adapting their business strategies to invest in workforce skilling. One of our customers, ENGIE, a multinational utility company and global reference in low-carbon energy and services, partnered with AWS Training and Certification to develop a cloud skills training program to equip its decentralized team of 4,000 IT professionals with proper skill sets to act on the data in a timely manner. Because of the training, ENGIE’s engineers adopted more advanced functionalities such as machine learning for predictive maintenance models used at its power plants. “Every time someone is trained, that person can start innovating,” says Frédéric Poncin, head of the Cloud Center of Excellence at ENGIE. “They can help transform the old system into a brand-new cloud-native application.”
Every time someone is trained, that person can start innovating. They can help transform the old system into a brand-new cloud-native application."
—Frédéric Poncin, Head of the Cloud Center of Excellence, ENGIE
Organizations are also recognizing the importance of cloud technology beyond the IT department and are now focusing on developing cloud expertise throughout different departments, including finance, sales, human resources, marketing, and administration. Volkswagen, for example, invested in strengthening its employees’ cloud knowledge and skills using a cloud-centric framework. This resulted in reduced time to market and improved cross-team collaboration.
Now is the time to invest in workforce upskilling and reskilling to maximize opportunities to grow your business with AI and ML. Our online learning center, AWS Skill Builder, offers digital training built by experts at AWS, including more than 80 courses and learning resources on AI and ML. With the emergence of generative AI, AWS is empowering learners and decision-makers to build their knowledge and skills in generative AI with many new trainings. Organizations can deepen and accelerate their talent transformation with a Team subscription offering unlimited access to hands-on, game-based learning, such as AWS Cloud Quest: Machine Learning Specialist. Leaders can partner with AWS Training and Certification to utilize these resources and more to enable continuous development of new skills across their organization to drive innovation and growth for their business.
For leaders and decision-makers who want to learn even more about generative AI, the Generative AI for Executives video series provides a high-level picture what generative AI is and how it can address your business challenges, drive growth, and why it has the potential to revolutionize industries.