Upskilling Tomorrow's ML/AI Leaders: In Conversation With Amazon's Rajeev Rastogi

Machine learning (ML) and artificial intelligence (AI) have become pivotal forces, reshaping industries and driving innovation across the globe. To harness the potential of this transformative technology, organisations are navigating the complex challenge of ensuring that their workforce is not only well-versed in ML and AI but also equipped to adapt to the ever-changing demands of the field. Rajeev Rastogi, Vice President of International Machine Learning at Amazon, shares insights into how organisations can bridge the skills gap and prepare their talent for a future in tech.

Given the rapid advancements in ML and AI, how can organisations ensure that the curriculum and content of their employee training or upskilling programmes remain up-to-date and relevant to the evolving needs of the industry?  

Over the past decade or two, the trajectory of machine learning has been on a continuous upswing, gaining traction across various industries. It is being adopted aggressively by manufacturing, financial services, retail, transportation and agriculture, healthcare sectors and many more. Machine Learning (ML) has become a significant lever in solving customer problems and the demand for machine learning roles is expected to increase significantly across employers. A study by the World Economic Forum acclaims that AI, machine learning and data segments will be the top emerging job roles in India over the next five years while the talent pool is expected to remain the same.

Machine Learning is poised to revolutionise workplace trends by enabling data-driven decision-making, personalising experiences and automating routine tasks. It will foster continuous skill enhancement, optimise remote work, detect bias and enhance cybersecurity. As organisations leverage machine learning for insights into employee performance and engagement, virtual collaboration and customer support, workplaces will become more efficient, innovative and inclusive. However, this transformation demands a shift in employee interaction with technology and a commitment to ongoing learning to navigate the evolving landscape effectively. 

Given the swift pace of advancements, aspirants and professionals in this field must continually enhance their skills to align with industry trends. Organisational upskilling emerges as a pivotal strategy to stay attuned to these trends, benefiting from an inherent understanding of the industry's demands.

Upskilling efforts can be categorised in two keyways: firstly, catering to existing market demands. This entails equipping employees with the technical acumen required to navigate intricate ML projects effectively. Secondly, there is a forward-looking approach, preparing the workforce for the challenges of tomorrow. Here, leveraging data emerges as a strategic tool. Organisations can tap into a wealth of data to discern patterns that forecast upcoming trends. Moreover, mining insights from the workforce's experiences can inform data-driven decisions, aiding in the selection of pertinent training programmes for upskilling initiatives. This dual approach, anchored in organisational foresight and data-driven strategies, stands as a potent means to bridge the skills gap and stay aligned with the dynamic landscape of ML.

What strategies can organisations employ to bridge the gap between academic learning and real-world industry demands to prepare the talent for a future in tech? 

Machine learning (ML) is an emerging artificial intelligence (AI) technology which is being adopted aggressively by retail, transportation (especially airlines), and financial services companies operating in India. A study by World Economic Forum acclaims that AI, machine learning and data segments will be the top emerging job roles in India over the next five years. Almost a quarter of jobs (23 per cent) are expected to change in the next five years through growth of 10.2 per cent and decline of 12.3 per cent (globally). Around 61 per cent of companies in India will think broader applications of ESG (environment, social and governance) standards will drive job growth, followed by increased adoption of new technologies (59 per cent) and broadening digital access (55 per cent).   

According to NASSCOM’s ‘State of Data Science & AI Skills in India’ 2023 report, India has an estimated installed talent pool of 416K (August 2022) while the total demand for talent stood at 629K. The gap between total demand and supply is 51 per cent. India is expected to have a total demand of over 1mn professionals by 2026. This situation will create a churn for the industry because there would be multiple opportunities available for the fresh graduates, but students might not have the required skill set for these. 

Focusing on practical problem-solving, organisations can host events, workshops and competitions that foster teamwork, innovation and creative thinking. These events simulate real-world scenarios, prompting participants to collaborate on effective solutions within time constraints. This mirrors the challenges prevalent in the tech industry. 

Mentorship programmes also play a pivotal role in preparing students for the tech industry. This personalised approach bridges the gap by connecting theoretical knowledge with real-world applications, thereby cultivating a strong professional network. To address the fast-paced nature of technological advancements, organisations should advocate for continuous learning. By granting students and employees access to online platforms, courses and resources, they can acquire the latest skills and stay abreast of industry trends throughout their careers.

How do organisations collaborate with other industry stakeholders, academia and research institutions to strengthen the ecosystem for ML/AI /Generative AI talent development?

There are many initiatives and programmes in the market that aims to bridge the gap between demand and talent. The collaboration between industries and academia facilitates the exchange of knowledge, expertise and resources. Joint research projects and sponsored programmes allow organisations to leverage cutting-edge research while providing academic institutions with practical challenges to tackle. These partnerships not only advance technological progress but also nurture a diverse pool of skilled individuals who collectively shape the future of AI and its applications. Collaborations between academia and the industry is important to understand the needs of the industry so that students can be prepared accordingly with a dynamic curriculum and pedagogy.

What are some metrics to assess the progress of the impact of tech-driven L&D initiatives? 

Learning and development (L&D) initiatives are essential for enhancing the skills, knowledge and performance of employees. Depending on the objectives and indicators, organisations can use different methods and tools to evaluate L&D initiatives. Some common methods include surveys, tests, interviews, focus groups, observations, feedback forms, or self-assessments.

At Amazon, to assess the effectiveness of various upskilling and well-being initiatives, we employ metrics such as the Net Promoter Score (NPS), alongside qualitative narratives provided by participants of programmes. Amazon utilizes a real-time feedback platform called 'Connections,' enabling employees to confidentially share their programme experiences. The senior leadership team then analyzes the data to formulate and implement strategies while addressing the feedback received. 

In terms of scalability and reach, how does Amazon plan to expand the impact of its initiatives to reach a broader audience and create a more widespread talent pool in ML/AI?

In 2021, Amazon introduced Machine Learning Summer School (MLSS) for students from notable Indian institutes like IITs and IISc with the aim to nurture ML talent in India, enhancing readiness for market opportunities. That year, 3500+ students registered, with 300+ participants selected. The programme expanded yearly, engaging final-year students across degree programmes. By 2022, over 17,500 students registered and 2880 were chosen. The third edition aims to include even more participants.

Starting from 20 campuses, the programme now spans to include engineering students enrolled in any recognised institute in India, passing out in 2024 or 2025, projecting similar or even greater engagement. We could see the zeal and need for upskilling talent across, and we would take this opportunity to leverage upskilling in more countries in the coming years. The goal is to gradually expand and benefit students worldwide. We envision taking this programme to maximum students across countries.