Nowadays, the use of artificial intelligence is increasing rapidly with the advancement of multiple different technologies, the more technologies are evolving the more there is a need for artificial engineers and AI skills. AI skills have become important among engineering students due to the growing demand for AI professionals in the technical industry.
Therefore, in this article, detailed knowledge has been provided about artificial engineers and the 10 must-have AI skills for engineering students so that they can enhance themselves with the increase in AI technologies and can contribute to the advanced AI technologies with AI skills.
What is an AI?
AI is abbreviated as Artificial Intelligence which is technically a branch of computer science of making machines or software that can think like humans. AI technologies are used for doing multiple works within a few minutes or seconds like recognizing the pattern, making several decisions, and processing large data within seconds. The main motive of Artificial Intelligence is to do the things which are considered to be “Smart”.
Who are AI Engineers?
Artificial Intelligence (AI) Engineers are the engineers who are responsible for creating, programming, and training the complex networks of algorithms that make up AI so that they can function like a human brain. AI Engineers require combined expertise in the fields of Software development, programming, data engineering, and data science. Every engineering student needs multiple Artificial Intelligence skills to excel in their jobs and to develop various different AI applications.
10 Must have AI Skills for Engineering Student
There are multiple different Artificial Intelligence skills that every engineering student should learn. Some of the 10 must-have AI skills are mentioned below:
1. Programming Languages
There are many different programming languages which are there in the IT department. Some of the most important programming languages that every software engineer needs to learn are mentioned below:
- Python is the most popular programming language used for developing different algorithms, working on the data, and used in implementing models. In Python for AI development, some of the libraries that are commonly used are- Keras, Tensorflow, and PyTorch.
- Java is another popular language in the IT field for AI development. They consist of strong memory management and the breadth of high-quality machine learning libraries. Some of the use cases of the Java language is to use them building websites, chatbots, fraud detection, and so on.
- C++ programming language provides an ability to execute high-level applications with a relatively low computational cost which makes the language suitable for neural network computation and machine learning. The libraries which are used in machine learning in C++ are Shark and mlpack.
2. Data engineering and modelling
Data is the basic foundation of Artificial Intelligence and the software engineers need to have a deep understanding of data modelling and engineering. The AI engineers should know how to acquire, clean and transform the data into a suitable format for the analysis of knowledge of SQLand NoSQL databases which is important for query and meaning the big datasets. Some of the tools which are mainly used for data processing in AI projects are Apache Spark, Hadoop, AWS S3.
3. Machine Learning Models
The knowledge of the machine learning models and the algorithms are very important for the software engineers to upskill themselves. The knowledge about the machine learning models includes the understanding supervised and the unsupervised learning techniques as well as having a deep knowledge about the algorithms such as the convolution neural networks and the recurrent neural networks. The Artificial Intelligence Engineers should also know how to evaluate the performance of the machine learning models by using the metrics such as precision, recall, accuracy, F1 score, Mean absolute error and root mean square deviation. The basics of machine learning can be learned by using machine learning fundamentals with python.
4. AL and ML services
AI and ML services are the types in which the software engineers should be familiar with the machine learning and AI services which are offered by the main providers such as Azure, Google Cloud Platform and AWS . These AI and ML services provide the pre-built models, tools for developing the applications and APIs without the need for over coding.
5. AI Security
Every engineers should learn about how to maintain strong security and privacy measures as it is an important part as the use of AI bring new type of security vulnerabilities and it is the responsibility of every engineer to maintain the confidentiality, availability and integrity of data and by having the knowledge and understanding of the rules and regulations related to data protection such as General data protection regulation and by implementing secure AI frameworks which coordinates with these regulations. The engineers should also have the knowledge about the different encryption methods and secure the AI models development practices.
6. AI Deployment and DevOps
When developers deploy the AI applications then it involves the collaboration with the DevOps team by making sure that there is a smooth integration and deployment. Therefore software engineers should know how these tools should be used like the Kubernetes and Docker uses containerization, configuration management and the orchestrating deployments. They are mainly responsible for maintaining and monitoring the performance of the AI system post deployment. Thus every Artificial Intelligence engineer should have the AI deployment and DevOps skills for properly executing the deployment process.
7. Big Data Analysis
Big Data Analysis is a process where the AI engineers need to analyse the big sets of data and extract the meaningful insights from them which involves using the large data tools such as Apache Flink, Google Cloud Platform and to manipulate and query the large datasets. The big data fundamentals with the PyShark course which covers multiple principles behind big data analysis.
8. Teamwork and Decision making
Teamwork and Decision making are some soft skills which every engineers should know if they want to upskill and grow in their career as teamwork helps in working with a team towards a common goal and work in a collaborative environment while decision making is a skill to develop ethical decision making skills to navigate the complex ethical dilemmas in AI. These two are the most important soft skills which every engineer should know as it helps them to enhance their soft skills because in the technical world not only technical skills but also these soft skills plays an important role.
9. Logical Reasoning and Analytical skills
Among software engineers Logical reasoning is a crucial skill which every engineer should have. The engineering students should enhance their logical reasoning skills by developing AI systems and can make some intelligent decisions. The analytical skills are the skills which every engineering student should know. Some of the analytical skills are- interpreting the data, identifying the data and drawing some meaningful conclusions from those trends and data. The engineering student should develop the strong analytical skills to analyse the AI models performance by identifying the areas of improvement and making the data driven decisions. By using and working upon these skills engineering students can enhance their ability to think and make right decisions.
10. Algorithmic Thinking
Logical and algorithmic thinking involves breaking down the complex problems into smaller, manageable steps. Every engineer should know about logical and algorithmic thinking as it helps the engineers to learn the programming languages easily and after learning these programming languages they can apply their logical thinking in building real applications and solving real world problems. This skillset will help the engineers to optimise the processes in developing things and improving the efficiency.
Conclusion
These AI skills are very important in the technical field because these AI skills are in high demand in the AI industry and these skills are needed for implementing some of the advanced algorithms in Artificial Intelligence. Therefore by learning the AI skills engineers upskill themselves for better opportunities in the technical industry and to develop some high quality of AI technologies. Thus in this article a complete overview has been provided about the AI skills and the top 10 AI skills which every engineer should know.
FAQs
What do you mean by AI engineer ?
Artificial Intelligence (AI) Engineers are defined as the engineers who are responsible for developing, programming and training the complex networks of Data structures and algorithms which make up Artificial Intelligence so that they can function like a human brain.
Why do engineers need AI skills ?
Every engineering student needs the AI skills to succeed in their career as these skills play a very important role in the enhancement of the career of engineers. By continuously learning the skills and upskilling engineers can find better opportunities and can find solutions for the real world problems.
Name some of the most important AI skills ?
Some of the most important AI skills are learning the most in demand programming languages, learning about machine learning models, data engineering and modelling, should work on analytical skills, should learn logical and algorithmic skills, teamwork and collaboration are the important AI skills.
