How to Make a Career in AI and Machine Learning?

Khushi Bali
Khushi Bali October 22, 2022
Updated 2022/10/22 at 4:05 PM

In today’s ever-changing high-tech world, new technologies such as AI, Data Analytics, and Machine Learning have dominated almost every field. With IT companies constantly introducing new innovations, the potential for developing new technologies is limitless.

Organizations are already leveraging the potential of AI and Machine Learning to streamline internal processes and analyse data on everything from customer habits to building a knowledge pool in order to ensure overall growth. According to data collected by The International Data Corporation, the AI market in India could reach $7.8 billion by 2025. From a career standpoint, it is expected that the ML Engineer will be the fastest growing role through 2023. With open positions for ML engineers being half that of other roles.

Here are some of the skills needed for a successful career in the field of AI and Machine Learning. While acquiring these many skills may be difficult, taking a focused approach based on one’s passion may yield positive results.

Statistical Expertise:

AI is a stats-driven game, and to be an expert, you must understand and analyse statistics in order to decode complex algorithms. Finding patterns in the majority of available data is a necessary practise for an AI developer. To succeed in this industry, statistical prowess in examining and evaluating large algorithms is required.

Programming Skills:

A thorough understanding of programming languages is essential. Understanding various forms and scripts of programming, such as Python, Java, R, C#, C++, and Julia, could be a goal for any aspirant wishing to enter the AI Industry, as the programming tools assist in achieving the desired results.

Optimization Skills:

AI and ML professionals must understand how to optimise development and production environments for performance, scalability, and reliability, with a focus on continuous improvement and infrastructure optimization to build and train models.

AI professionals must be able to deal with complex data and algorithms that must be distributed across the entire cluster. As a result, distributed computing skills are essential, 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 like Apache Hadoop and Apache Spark would be advantageous.

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