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What is the academic curriculum for a successful career in AI?
Aruna Kolluru
Q.
Artificial Intelligence is not a profession, it’s a technology that enables the extraction of insights from data. AI requires varied skills and different roles throughout the data science life cycle. There are many specialised roles in AI, the most characteristic ones being:
- AI Architects determine what business problems to solve. They choose the right technology, frameworks, and architect solutions to solve business challenges.
- Data engineers manage the data sources and prepare the data. Well, we can argue that the Data Engineer is not part of AI, but I have included it as it’s an important piece of the puzzle for AI to be successful. A data engineer is a comprehensive data specialist who prepares data and makes it available within your organization. By extracting information from different systems, transforming, cleaning data, and combining different sources into a working database, data engineers become an important piece of the puzzle in the AI process. They analyze and cleanse the collected data from the various systems and tools, used in an ecosystem. To uncover the hidden treasures in a company's data, these individuals need to deeply understand business processes.
- Data scientists train the models using the prepared data. They use machine learning and predictive analytics to collect, analyze, and interpret large amounts of data to gain insights that go beyond statistical analysis. A data scientist builds machine learning or deep learning models to help organisations improve efficiencies.
- AI or Machine Learning Engineers deploy the models. Once the models are built, the process is very similar to the software development lifecycle. The role of an AI/ML engineer is to develop deployable models from models developed by data scientists and integrate the models into the final product. AI engineers also develop secure APIs for delivering models on demand.
- AI Strategists work in research or real-world solutions. They should have hybrid skills in both technology and business. A good AI strategist understands the power of both technology and business; they must be able to explore the art of the possible as the means of transforming the business. They also identify the important AI projects for the organisation and strategize the AI journey for the organization. In my experience, the biggest gap we have in the industry at the moment is the AI Strategists.
Most often the perception is that people working in AI need a Ph.D. It really depends on what exactly the person is interested in. Skills required for different roles in AI vary but they typically include mathematics (statistics, linear algebra, and probability), programming languages (python, java, R, C++), neural network architectures or frameworks, and most importantly domain or industry knowledge.
For a high school student considering a career in the AI space, I would suggest focusing on and learning Mathematics and Programming. It’s important to get your hands dirty so, do some Python projects. Get yourself familiar with some Machine Learning or Deep Learning frameworks like Sci-kit learn, Pytorch, or TensorFlow and try to apply them in solving real-world challenges.
It is important to have a degree in Computer Science which helps understand the basics and provides a strong foundation. A Master's in Data Science will add to your knowledge and give you the tools and skills required for developing a career in Data Science. Also, it is essential to explore an industry you are interested in and look at how AI is transforming that industry. There are a lot of massive open online courses (MOOCs) and university programs with a focus on AI in certain verticals. And lastly, If you are interested in research and improving and building algorithms then you will definitely need a Ph.D. All these if you would like to have a formal education. I have also seen a lot of people without formal education excelling in the AI space by learning from MOOCs.
"There are a lot of massive open online courses (MOOCs) and university programs with a focus on AI in certain verticals."
Aruna Kolluru is passionate about business, technology, and emerging technologies. She helps organizations to visualize the art of possible, architect, and build solutions with emerging technologies. Before joining EMC & Dell Technologies, Aruna held various senior roles for IBM & Oracle. She has over 21 years of experience in the industry on varying technologies including AI, Blockchain, Deep Learning, Computer Vision, IOT Big Data & Analytics, and Enterprise Architecture.
Aruna Kolluru
"A lot of people without formal education excelling in the AI space by learning from MOOCs."
Chief Technologist, AI, APJ
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