In the last year, thousands of workers have reportedly received artificial intelligence (AI) training from technology product and IT services companies. Still, India is witnessing an AI talent crunch, and currently needs more employees for essential AI products and services.
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These tech firms are also making significant investments in staff upskilling to tackle the AI talent crunch. Examples to illustrate:
- Tata Consultancy Services has trained 350,000 workers in artificial intelligence (AI).
- Infosys claims that eight out of ten workers are prepared for AI.
- Accenture is upskilling 250,000 workers in AI.
- HCLTech has trained 50,000 workers in AI.
- Microsoft India intends to train 2 million workers in AI by 2025.
- IBM has made a global commitment to train 2 million workers in AI by 2026.
There are many essential skills in artificial intelligence, such as creating, executing, and overseeing artificial intelligence applications and systems. To successfully navigate the intricacies of AI development, AI specialists need various competencies, from a core understanding of programming and software development to computer vision. Additionally, the real-world impact of AI models is facilitated by the seamless integration of these models into production systems, which is made possible by skill in deployment and DevOps processes. AI specialists must constantly broaden their skill sets and adjust to new technologies and approaches in this dynamic industry to foster innovation and meet changing problems.
Essential AI Skills that Will Help Overcome AI Talent Crunch
Domain knowledge
Domain knowledge refers to specialised expertise in specific fields where artificial intelligence (AI) is applied, such as healthcare, finance, robotics, and more. This deep understanding of the domain’s intricacies and nuances is invaluable for AI professionals as it enables them to tailor AI solutions to address specific challenges and opportunities within the industry. For instance, in healthcare, domain knowledge allows AI practitioners to navigate complex medical data, understand regulatory requirements, and design AI-driven healthcare systems that enhance patient care and outcomes. Similarly, in finance, domain experts can leverage AI to analyze market trends, optimize investment strategies, and detect fraudulent activities with greater precision and effectiveness. Furthermore, domain knowledge facilitates effective communication and collaboration with domain experts, enabling AI teams to seamlessly incorporate domain-specific insights and requirements into their projects.
Project management
Project management skills are essential for overseeing AI initiatives effectively from conception to completion. This encompasses various aspects of project management, including planning, resource allocation, risk management, stakeholder engagement, and monitoring progress. Moreover, effective project management fosters collaboration and communication among team members, mitigates risks and challenges, and ultimately maximizes the success of AI initiatives. By leveraging project management methodologies and tools, AI professionals can streamline project workflows, track progress, and adapt to changing circumstances, thereby driving the successful implementation of AI solutions in diverse domains.
Machine Learning and Deep Learning: AI practitioners must thoroughly understand the fundamental ideas behind deep learning structures, machine learning algorithms, and their applications. Proficiency in reinforcement learning, supervised and unsupervised learning, and neural networks facilitates the creation of advanced artificial intelligence models that tackle intricate issues.
Programming Languages: Building and modifying AI models requires a solid understanding of Python, Java, R, and C++ languages. These languages offer a flexible toolbox for working with data, implementing algorithms, and creating AI applications in various fields.
Data Modeling and Analytics: Proficiency in data wrangling, cleansing, analysis, and visualization is crucial for working with massive datasets in AI development. AI experts can uncover valuable insights and patterns from data, which guide the design and optimisation of AI models, thanks to their expertise in data preparation, feature engineering, and exploratory data analysis.
Libraries and Frameworks: Familiarity with well-known AI libraries and frameworks like TensorFlow, PyTorch, and sci-kit-learn makes developing and implementing AI solutions easier. These libraries speed up development and increase productivity by offering pre-built functions, algorithms, and tools for creating, honing, and assessing AI models.
Mathematics and Statistics: Understanding AI algorithms and interpreting model results requires a strong foundation in mathematics and statistics, including ideas from probability theory, linear algebra, calculus, and statistical inference. AI practitioners are better equipped to create and optimise algorithms, evaluate model performance, and make data-driven decisions when they understand mathematical concepts.
Conclusion
In summary, there are slightly under 21,000 active, available Indian talent pool members who possess the necessary training, expertise, and exposure to fundamental AI. This graph highlights the enormous potential and current limitations in the Indian AI ecosystem. Despite its apparent small size, the pool of qualified individuals is a growing community of AI experts ready to push innovation and make important contributions to the field. However, coordinated efforts in professional development, training, and educational programs are desperately needed to grow further and broaden this talent pool.
India may use the revolutionary potential of artificial intelligence to address critical issues, boost economic growth, and promote inclusive development across all sectors by investing in the development and nurturing of AI talent. To fully realise the promise of AI-driven innovation and bring about a better future for everybody, it will be necessary to support and empower the upcoming generation of AI experts as the Indian AI environment develops.
