Artificial intelligence, or AI, is taking off like wildfire. From marketing to entertainment, every industry is relying on AI to take innovative solutions to the next level. According to a report, the AI market is expected to grow at a CAGR of more than 38% by 2030 and reach $1,597.1 billion.
While AI and digitization are predicted to make 85 million jobs obsolete, they’re also expected to create 97 million new jobs by 2025. And it won’t be a surprise if the role of AI engineers leads this evolution.
Here are seven skills that you must master to become an A-class AI engineer.
1. Strong Programming Skills
Programming languages lay the groundwork for everything to become AI-powered – creating computer systems that make intelligent predictions and decisions. The most common requirement for anyone aspiring to be an AI engineer is the ability to program and run codes. These can be in any language, including but not limited to C, C++, Python, and others. The main idea is to thoroughly understand how programming contributes to the development of algorithms that can be used to make predictions, for instance.
AI engineers with excellent programming skills ensure that:
- Relevant AI models are created using the suitable algorithms
- Data is preprocessed
- The compiled system is tested and debugged
- This AI system can be integrated into the existing system
2. Statistical and Mathematical Models
The foundation of AI is data, and to decode this data, AI engineers must have a thorough understanding of statistical and mathematical models. Although mathematics is only the tip of the iceberg in the AI revolution, it is unquestionably necessary. The most fundamental mathematical knowledge areas are numerical programming, graph theory, fuzzy sets, lambda calculus, statistical models, and probability. Math and statistics should be used to add value and solve problems that may arise when writing programs.
Aside from that, mathematics is required to choose the appropriate algorithm that works with the system. AI engineers must also monitor the performance of their systems, evaluate the model, and work on potential solutions using equations and numbers.
3. ML Algorithms and Deep Learning Frameworks
Machine learning is a subset of AI. To become an expert AI engineer, one must understand the intricacies of ML algorithms. AI is used to create intelligent machines, but ML algorithms perform real-world tasks such as image recognition, predictive analysis, and so on.
Deep learning, like ML, is a sub-segment of AI. Deep learning frameworks are required for AI engineers to build models that analyze large amounts of data and develop and deploy neural networks. These frameworks are essentially libraries that give AI developers access to tools for applications like automation, speech recognition, and image recognition. Thus, an aspiring AI engineer must know the ins and outs of ML algorithms and deep learning frameworks.
4. Understanding of Cloud and Database
Databases and cloud computing both support and power an efficient data storage system. The goal of AI is to analyze large amounts of data to make informed decisions. Thus, in order for an AI engineer to excel in the field, they must understand cloud computing and the operation of database management systems. Many AI applications are cloud-based, which explains the close relationship between AI and cloud computing. When the cloud meets AI, some functional possibilities include effectively managing data, updating it, and using it to gain real-time insights.
A database is simply an organized method of collecting data. This data is then used by AI systems to make decisions. Overall, the cloud and on-premise databases are both data-driven solutions that necessitate a thorough understanding of how to churn this stored data at the appropriate time for use in AI-driven systems.
5. Data Modeling and Natural Language Processing
Data modeling is a method of structuring data. This improves data quality and forms clusters required by AI systems for easy and efficient algorithm processing. Natural Language Processing (NLP), on the other hand, is a method of training software or systems to understand human input and respond appropriately – chatbot, for instance, is an excellent example of the same.
Data modeling is a subset of data analytics, whereas NLP is a subset of AI. To understand NLP effectively, an AI engineer must be well-versed in ML frameworks and libraries.
6. Familiarity with Big Data Technologies
According to Statista, global data creation will exceed 180 zettabytes by 2025. This is a massive and almost unbelievable amount of data that is being made available to the world. AI models are fueled by massive amounts of data to analyze patterns and understand what consumers are looking for. To do this effectively, engineers must have a firm grasp and understanding of big data technologies and how these data processing systems work; they must also learn how to use the data for further analysis.
AI and big data are inextricably linked because algorithms can be employed to make sense of massive amounts of data, decipher patterns within it, and devise strategies relevant to today’s consumers or audiences.
7. Problem-Solving and Critical-thinking Skills
AI engineers must possess problem-solving and critical-thinking skills to have a successful career in this field. These skills are required to create systems that can make decisions without human intervention. When designing such systems, an AI engineer must navigate through hindrances, for which problem-solving skills are critical. Once the appropriate solutions have been developed, they must be implemented in systems that require critical thinking.
An AI engineer works with large amounts of data, and analyzing and utilizing this data to develop high-tech systems is difficult. To deploy such systems to solve problems, excellent critical thinking skills are imperative.
AI engineers will be in high demand as AI spreads its wings across multiple industries. Ascentt provides hands-on experience and conducts bootcamps on such cutting-edge technologies to help you get started in the field of artificial intelligence.
Get in touch with us to work on these technologies in a vibrant work environment and steer your career in the right direction.