How to make a career in Artificial Intelligence and Machine Learning | The Financial Express

How to make a career in Artificial Intelligence and Machine Learning

While having a robust technical understanding of AI/ML is imperative, it is equally vital to build aspects of domain knowledge and human expertise to provide context to data.

How to make a career in Artificial Intelligence and Machine Learning
Responsible development of AI is necessary expertise.

By Srividya Kannan

New technologies like AI, Data Analytics, and Machine Learning have dominated almost every field in today’s ever-evolving high-tech world. With IT companies introducing innovations on a constant basis, the scope for the development of new technologies is limitless. 

Organizations are already harnessing the potential of AI and Machine Learning to streamline processes internally and analyse information on everything from customer habits to building a knowledge pool to ensure their overall growth. According to the data collected by The International Data Corporation, the AI market can touch up to 7.8$ billion in India by 2025. From a career perspective, through 2023 it is expected that the ML Engineer will be the fastest growing role with open positions for ML engineers at fifty percent of that of data scientists which were less than 10% in 2019. 

As more and more companies increasingly rely on AI and Machine Learning for their growth and functioning, a talent pool of new-age skilled innovators will be required to maintain projects and fill the talent gap in this growing industry. 

Here are some of the skills that you would require to make a successful career in the field of AI and Machine Learning. While it could be challenging to acquire these many skills, having a focused approach depending on one’s passion could lead to good outcomes.

Statistical Expertise: AI is a statics-driven game, and to be an expert, you need to have the capability to understand and analyze statistics to decode complex algorithms. Finding patterns from most of the information available is an essential practice for an AI developer. Therefore, statistical prowess in examining and evaluating large algorithms is a requirement to succeed in this industry. 

Programming Skills: A thorough knowledge of programming languages is of utmost importance. Understanding various forms and scripts of programming, including Python, Java, R, C#, C++ and Julia could be a goal for any aspirant that wishes to enter the AI Industry, as the programming tools help achieve the desired results. 

Optimization Skills: The AI and ML professionals need to understand– how to optimize development and production environments for performance, scalability and reliability with a focus on continuous improvements and optimising infrastructure to build and train models. 

Distributed Computing Skills: AI demands that professionals deal with complex data and algorithms that must be distributed across the entire cluster. Therefore, it is important to have distributed computing skills, including practical knowledge of AI hardware (GPUs and SSDs) and familiarity with public cloud Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). Experience managing systems such as Apache Hadoop and Apache Spark would be beneficial. 

Functional expertise: While having a robust technical understanding of AI/ML is imperative, it is equally vital to build aspects of domain knowledge and human expertise to provide context to data.

Additional skills: AI experts with deep knowledge of graph analytics, AI frameworks to ensure the quality of algorithms before pushing to production, and deep learning frameworks such as Tensor Flow and PyTorch are good skills to have. Again, responsible development of AI is necessary expertise. 

The career roadmap for someone with these skills is highly bright and could likely be on the path of AI/ML engineer, data scientist, developer to prominent data engineer. Aspects of composite AI and practices such as ModelOps (DevOps) and AI Engineering would be great bets for the future.

Since the industry requires a multi-skilled task force, the aspirants should be well-versed with several dynamics of AI and ML. To do so, one should be aware of their strengths, and the knowledge one lacks. Knowing the basics would allow aspirants to polish what they already know and help them acquire new skills. 

One of the best ways to do so is by working on live projects. After attaining a certain level of understanding of the industry, it is highly recommended to work on diverse projects that would help you gain exposure and face real-life challenges. As it is said, “Practical application of any learning can be considered the key to getting better at what you desire.”

The author is director, founder at Avaali Solutions. Views are personal.

Also Read: West Bengal state universities not using CUET for admission to UG courses

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