How to Become an AI Developer? [UPDATED]

• Blockchain Council
• September 17, 2024

Introduction

Artificial intelligence, commonly known as AI, is an emerging technology rapidly evolving and transforming various industries. It is revolutionizing how we live, work/communicate, and its relevance is expected to grow in the coming years. AI is nothing but the development of machines and computer algorithms in a way that they can perform different tasks as a human would.

The demand for AI technology is soaring, and as a result, many businesses and industries are investing heavily in developing AI systems. This has increased the demand for skilled AI developers who can create machine-intelligent algorithms and programs to meet business demands. Becoming an AI developer can be a highly lucrative career choice in today’s market, and it is an exciting field that offers endless opportunities for growth and innovation. This article will share everything you need to know about becoming an AI developer to develop a strategic plan for your career. We will also tell you the reasons why becoming an AI developer can be a profitable and fulfilling career choice in today’s world.

Understanding the Basics of AI

Before jumping into who an AI developer is, let’s understand the basics of AI. Artificial intelligence (AI) is a broad field encompassing various subfields working together to create intelligent machines. In this section, we will explore the different types of AI and their applications, along with the role of machine learning in advancing AI computational capabilities.

Explanation of AI and Its Various Subfields

AI is a subfield that can be considered a part of computer science, and its main focus is on successfully developing machine algorithms that can be used to perform or assist in tasks that humans usually do; this can include a variety of skills such as reasoning, problem-solving, and decision-making. AI encompasses many subfields, including:

Robotics 

Robotics is a field of engineering that deals with robot design, construction, and operation. Robots are machines that use sensors and algorithms to perform various tasks. Robotics is useful in many industries, like supply chain management and factories. The demand for robots is poised to enter other sectors once they are more developed and capable.

Natural Language Processing (NLP)

NLP is one of the most important developments that has been done with the help of AI that focuses on making machines capable enough to comprehend, describe, or even respond to human languages like English and various others too. NLP makes it possible for computers to understand the tasks given by humans in languages like English and perform tasks based on them. We can refer to chatGPT, sound diffusion, and Lamda when discussing successful NLP models, which are quite successful and well-known.

Computer Vision 

While using tools like google lens or language translators, you might have wondered about its working, and this is the ability of computer vision to analyze and describe the visual data around it that makes it possible. This is similar to human eyes in applications but uses cameras, sensors, and algorithms instead of retinas and optic nerves. Google is currently using this technology in its speech detection and translation software, and apple is using it to build its self-driving cars.

Types of AI and Their Applications

AI can be broadly classified into narrow or weak AI. Here is a detailed outlook on them.

Narrow or Weak AI 

When we talk about this type of artificial intelligence, the scope of it is limited as they are designed to perform specific tasks. Most of our everyday applications use his sort of AI to perform their tasks flawlessly. Its applications include personal assistants like Siri and Alexa, self-driving cars, fraud detection in financial transactions, and predictive maintenance in manufacturing processes.

Reactive machines

These AI systems are designed to operate only in the present moment, reacting to the current situation based solely on the input they receive. They cannot form memories or draw conclusions based on past experiences. Examples include Deep Blue, IBM’s chess-playing computer, and AlphaGo, the AI system developed by Google’s DeepMind that defeated the world champion Go.

Limited memory AI

Have you ever used a chat assistant like replica? Then you might be aware of how they develop their character over time with the help of the chats that happened in the past. They come under this category called limited memory AI. They learn from past experiences, and in some other cases, they also utilize historical data to draw connections and make decisions based on that knowledge. Other examples include recommendation systems online retailers use to suggest products to customers based on past purchases or self-driving cars that use data from their sensors to improve their decision-making capabilities.

Self-aware AI

These AI systems can learn from past experiences and understand their existence and purpose. They can reason about their thoughts and emotions and have a level of consciousness that allows them to adapt to new situations and think creatively. This type of AI is still largely hypothetical and remains the subject of much speculation and debate among researchers and philosophers.

The Role of Machine Learning in AI

Machine learning is the hotbed for development right now because of its application in making AI and its learning self-executed without programming specifically for every action it performs. It is the most significant driver of AI advancements in recent years. It has proven to be very important in areas where AI can make its footholds, like finance, speech, and pattern recognition, and more because of its self-learning abilities. Its applications are diverse and expanding, and machine learning plays a significant role in advancing AI capabilities.

So, Who is an AI Developer?

An AI developer is a software engineer or programmer who specializes in creating, designing, building, and maintaining artificial intelligence (AI) systems. These systems can range from simple decision-making algorithms to complex deep-learning networks that can recognize and interpret patterns in data.

AI developers work with data scientists and other professionals to develop AI solutions that can solve complex problems in a variety of industries. They may be responsible for designing and training machine learning models, developing algorithms to analyze data, and creating user interfaces for AI applications.

In addition to technical skills, successful AI developers also possess strong analytical and problem-solving abilities. They must be able to break down complex problems into their component parts and identify the key issues that need to be addressed. They also need to have a passion for learning and staying up-to-date with the latest advances in AI and machine learning.

Why Should You Be an AI Developer

High Demand for AI Developers: AI is one of the fastest-growing fields in technology, and the demand for AI developers is increasing rapidly. The AI industry is projected to grow at a rate of 38.1% from 2022 to 2030, and this growth is expected to create a significant number of job opportunities for AI developers.

Lucrative Salaries: AI developers are among the highest-paid professionals in the tech industry. The average salary for an AI developer in the United States is over $123,000 per year. As demand for AI developers increases, so does the potential for even higher salaries and benefits.

Opportunity to Work on Cutting-Edge Technologies: As an AI developer, you will have the opportunity to work on some of the most exciting and innovative technologies in the industry. You will be at the forefront of developing new AI applications, machine learning models, and deep learning algorithms, and you will have the chance to make a real impact on how these technologies are developed and used.

Variety of Career Paths: AI is a rapidly evolving field, and AI developers have the opportunity to work in a variety of industries, including healthcare, finance, transportation, and energy. AI developers can also work for startups, established technology companies, or research institutions, providing a range of career paths to choose from.

Opportunity to Solve Complex Problems: As an AI developer, you will be responsible for developing solutions to some of the most complex problems facing society today. AI is being used to solve a variety of problems, from predicting and preventing diseases to reducing energy consumption and increasing efficiency. By working in AI, you will have the opportunity to make a significant impact on the world and help solve some of the biggest challenges we face.

Now, let’s dive deeper into understanding how to become an AI developer.

How to Learn Programming and Tools for AI Development?

To become a successful AI developer, you must have a good grip on programming languages. Artificial intelligence is solely based on how well a person can program and apply mathematical knowledge to build intelligent algorithms that can compute and solve problems to ease human work. Using different tools to test the algorithms or making important additions also holds the same importance. We will now see what some popular languages are being used by AI developers and the tools that you should know about.

Popular Programming Languages Used in AI Development

Artificial Intelligence (AI) development requires expertise in programming languages that support different AI applications. AI is one of those fields where your success can depend on how well you program and solve the problems presented in front of you. While languages are just a tool to attain that goal, you should finally know all the major languages in this field. Some of the programming languages that you should know as an AI developer have been discussed below

Python

Python is one of the most popular and used languages when it comes to programming language for AI development. This is possible due to its simplicity and ease of accessing its vast library to make the process more efficient while building complex algorithms. Python’s libraries and frameworks, like TensorFlow, Keras, PyTorch, and SciPy, provide a solid foundation for AI and machine learning applications. Python is a beginner-friendly language that anyone can easily understand and also choose to start their programming journey with it.

R

R is another programming language used in AI development that has started gaining more fame and popularity in recent years. It can be considered, especially for data analysis and visualization and even for machine learning. R offers extensive libraries and tools for data analysis and modeling. R is complex to learn, so it is suggested to learn it once you have an acute idea of programming and programming languages.

Java

Java is a universal programming language in various applications, including AI development. Its scalability, portability, and efficient memory management make it ideal for developing AI applications. It is also a beginner-friendly language that you can choose to start your learning journey.

Understanding Data Structures and Algorithms

Once you start learning about artificial intelligence, there are various subjects you should be proficient in. Understanding data structures and algorithms enables developers to optimize AI applications’ performance, improve efficiency, and solve complex problems. Some essential data structures and algorithms for AI development include Arrays and Linked Lists, Hash Tables, and sorting algorithms.

Arrays and linked lists are data structures used to store data in an orderly manner. They are essential for organizing data in AI applications. Hash tables are data structures that keep key-value pairs, making searching and accessing data in AI applications easy. Sorting algorithms are essential for AI applications that organize data in specific ways. Some sorting algorithms include Bubble Sort, Quick Sort, and Merge Sort.

Familiarizing Yourself with Development Tools and Frameworks

Some multiple tools and frameworks can make the journey of AI development easy for you. These tools and frameworks provide a foundation for developing robust and efficient AI applications. Some of the popular AI development tools and frameworks include:

TensorFlow

TensorFlow is one of the most popular open-source libraries when it comes to machine learning and AI. It can be used to develop AI applications. You can develop and implement various features like speech and language processing for your application with the help of this library. You can easily build ML models, use pre-trained models, and more on tensor flow and take its help to further your skills in AI development.

PyTorch

PyTorch is a popular machine-learning library. It is mostly used to provide a python interface for applications that deal with deep learning. It is ideal for developing AI applications that require flexibility and speed. It is a fully-featured framework generally used to build deep learning modules with its help.

Keras

Keras is another important tool that is used by most experienced AI developers. It is a high-level neural network library that makes it easy to develop AI applications and their interfaces. It is an API, so it can be used to work over different applications like TensorFlow, too, while it also makes the implementation of neural networks easier and simpler.

To become proficient in AI development, one must learn programming languages like Python, R, and Java, understand data structures and algorithms, and familiarize themselves with development tools and frameworks like TensorFlow, PyTorch, and Keras. They help you understand the topics profoundly and significantly create more career opportunities for you in the long run.

Skills in Data Science and Machine Learning

Data science and machine learning are two rapidly growing fields that have become essential for businesses of all sizes. They involve extracting knowledge and insights from data, which can be used to make better decisions and create more efficient processes. Developing data science and machine learning skills can open doors to career opportunities and help individuals become more competitive in the job market.

Understanding Statistics and Probability

Statistics and probability are the foundation of data science and machine learning. Proper knowledge of these subjects is essential for describing, detailing, and processing the data properly. Some key topics under this heading include descriptive statistics, used for measures like mean, median, and standard deviation to describe the data’s characteristics. Inferential statistics refers to using a small sample to look for patterns and make predictions based on them about a larger population. The probability distribution is another important subject that deals with the distribution of data and can be used to make accurate assumptions about future outcomes. Then there is Hypothesis testing, which tests predictions about the relationship between variables in a dataset.

Learning How to Manipulate and Analyze Data

Once you understand the basics of statistics and probability, the next step is to learn how to manipulate and analyze data. The manipulation of data refers to using different tools and software and tools to find patterns and connections in raw data. Some key topics under this heading include Data preprocessing, visualization, exploratory data analysis, feature engineering, and data mining.

Data preprocessing refers to cleaning and formatting data to be analyzed, while data visualization uses graphs and charts to help communicate findings. Exploratory data analysis is understanding the patterns and relationships in data. Feature engineering. Feature to select and transform the most relevant parts for machine learning models. Lastly, Data. Lastly, algorithms extract patterns and relationships from large datasets.

Building Models Using Machine Learning Algorithms

With a solid foundation in statistics, probability, and data analysis, the final step is to learn how to build and apply machine learning models. The task of these models is to use algorithms to identify patterns in data and make assumptions about events based on that. Some key topics under this heading include supervised learning, unsupervised learning, regression, classification, and clustering.

Supervised learning is done when you have a dataset that is properly marked. This data is then used to train a model that can make or find patterns about aw or unmarked data. Unsupervised learning is identifying patterns in data without the use of labels. Regression refers to building models that predict continuous outcomes. Classification means building models that predict discrete outcomes. While clustering is grouping similar data points based on similar metrics

Developing data science and machine learning skills is a long-term process that involves learning and practicing across multiple domains. By understanding statistics and probability, learning how to manipulate and analyze data, and building models using machine learning algorithms, individuals can build the foundation to pursue a successful career in these fields.

Gaining Hands-on Experience

One of the most important aspects of becoming proficient in artificial intelligence (AI) is gaining hands-on experience. Hands-on experience enables an individual to understand the practical implementation of AI, which cannot be achieved by studying theoretical concepts alone. This section will discuss various ways to gain hands-on experience in AI.

Participating in AI Development Projects

When it comes to gaining experience in AI, there is no better way than to spend some time engaging in AI development projects. It allows one to work with experienced professionals who can guide and mentor the individual. Moreover, working on real-world problems enables individuals to learn about the challenges and limitations of AI. There are various ways to participate in AI development projects, such as:

Internships 

Many companies offer AI internships, which provide an opportunity to work on AI projects and learn from experienced professionals. Internships are a great way to gain hands-on experience and build a network. Internships also help you show expertise when applying for a job as an AI developer.

Hackathons 

You can also participate in hackathons to meet new people and test your skills. Hackathons are events where developers come together to solve problems and build projects. Participating in AI hackathons can provide an opportunity to work on AI projects and see what the others in the field are up to.

Freelancing 

Freelancing allows one to work on AI projects independently or with teams remotely. Freelancing can be a great way to build a portfolio and gain experience. Freelancing can support you financially to work on your skills while you find a suitable job as an AI developer.

Building Your Projects to Gain Experience

Building your own AI projects is another way to gain hands-on experience in AI. It enables individuals to work on problems that interest them and learn at their own pace.

Online Tutorials and Courses 

Many online tutorials and courses provide step-by-step guidance on building AI projects. Online learning platforms are one of the best ways to get a detailed course on AI. While courses on these platforms can be a great way to start, you must look for professional courses afterward. Blockchain Council is one of the most trusted platforms for certifications that can help you find a job or build practical knowledge for your career. They have a course on artificial intelligence curated by experts in the field and first-hand knowledge of the subject.

DIY Kits 

DIY AI kits provide an opportunity to build AI projects with pre-built components. DIY kits are very good for beginners who want to understand how the implementation of AI looks at various levels of development. These DIY kits provide you with all the information you will need while building a project and instructions to work on it.

Collaborating with Peers

Collaborating with peers interested in building AI projects can be a great way to learn and gain hands-on experience. Collaborating with peers can provide an opportunity to work on problems that interest all parties and learn from each other. It will help you understand the gaps in your learning and know about new skills and tools simultaneously.

Contributing to Open-Source AI Projects

Contributing to open-source AI projects is another way to gain hands-on experience in AI. Open-source projects provide an opportunity for you to interact and learn from pioneers in the field. Some different tools and frameworks allow you to contribute to multiple open-source projects:

GitHub 

GitHub is a platform owned and managed by Microsoft, and it is also one of the major platforms used by developers to collaborate on projects. Many open-source AI projects are available on GitHub, allowing one to contribute to real-world tasks. You can easily create a GitHub profile; if you are a student, you will get additional perks like premium access. GitHub is also a great way to document your programming journey and show what projects you have worked on.

Kaggle

Kaggle is a place where you can always find famous data scientists tackling and solving real-world issues by collaborating with them. Kaggle hosts AI competitions, providing an opportunity to work on AI projects and compete with other data scientists. Working here can land you opportunities in AI that you were looking for and also learn about the new developments happening in the field.

AI Communities

AI communities such as TensorFlow and PyTorch provide an opportunity to contribute to AI projects and learn from experienced contributors. AI communities also offer resources and support for individuals interested in AI. Gaining hands-on experience is essential to becoming proficient in AI, and these communities can help you in doing that.

Networking & Staying Up-to-Date with Recent Developments

Online Communities and Forums

Online groups are great resources for discovering new technology, best practices, and the latest trends. These communities are a great way to look for people who have been contributing to this field for a long time and take some valuable lessons for your journey ahead as an AI developer.

Some Popular Online Communities and Forums include:

• Reddit

This platform has several AI-related subreddits, such as r/artificial, r/MachineLearning, and r/DeepLearning. These communities are ideal for keeping track of the latest news and developments in the field.

• LinkedIn Groups

There are numerous AI-related groups on LinkedIn that you can join. These groups offer a great platform for connecting with other professionals, sharing knowledge, and engaging in discussions.

• Kaggle 

This platform hosts data science competitions and offers a community of data scientists who share their knowledge and insights.

Events and Conferences

Another great way to stay up-to-date with AI developments is by attending industry events and conferences. Events like this are considered a great way to learn about the top experts in the field and their insights on various problems that you will have to face during your development journey.

AI World Conference & Expo – This is one of the largest AI conferences in the world, and it brings together thousands of AI professionals, vendors, and researchers.

Neural Information Processing Systems (NeurIPS) – This annual conference focuses on machine learning and computational neuroscience.

Applied Machine Learning Days – This popular conference focuses on applied machine learning and includes talks, workshops, and networking events.

Blogs and Newsletters 

Several AI-related blogs and newsletters offer insights and analysis on the latest trends. Some popular ones include the AI newsletter from Forbes, OpenAI’s blog, and the Google AI blog. These blogs and newsletters are free; you can check them whenever you want and keep them updated. Keeping up-to-date with AI developments requires an ongoing commitment to learning and connecting with industry experts. Joining online communities and forums, attending industry events, and staying engaged with the latest research and news are all critical strategies for staying ahead of the curve in this fast-paced field.

Benefits of Learning with Online Courses & Certifications 

Several AI online courses and online AI certificates can help you understand the subject, inform you of any new developments, and train you accordingly. Here are some of the most significant benefits of using online courses and certifications:

• Accessible Learning

Online AI developer certifications are one of the most preferred ways of learning nowadays because of their accessibility, that is making it convenient for learners to fit the coursework into their schedules.

• Self-paced Learning

Online courses allow learners to work at their own pace, which can be especially beneficial for those who need more time to grasp difficult concepts.

• Interactive Learning

Many online courses offer interactive learning experiences, such as quizzes, assignments, and projects, which can help learners reinforce their understanding of the material.

• Expert Led Learning

Online certifications providers like Blockchain Council have Industry experts designing these online certifications with real-world AI development experience. This can provide learners with valuable insights into current industry practices and trends.

• Comprehensive Curriculum

Online courses can offer a comprehensive curriculum that covers all the essential topics needed to become proficient in AI development, including programming languages, algorithms, machine learning, and deep learning.

• Cost-Effective

Online courses are often less expensive than traditional classroom-based courses, making them an affordable option for learners looking to develop their skills in AI development.

Looking to shine in the AI domain? Enroll today for our Certified Artificial Intelligence (AI) Expert certification and lead the digital domain!

Conclusion

AI is a rapidly growing area, and with the growth, there is a demand for creative and passionate individuals dedicated to the job who don’t hesitate to take risks. Becoming an AI developer offers many benefits, including high salaries, job satisfaction, intellectual challenges, and endless possibilities to contribute to creating innovative solutions that shape the future. Moreover, AI is a field of continuous learning, which means that developers can always work on new projects and enhance their skills, making it a great opportunity to have a fulfilling career. Pursue your passion for AI by obtaining a certification or degree, attending workshops or lectures, and participating in online courses. Remember that perseverance is key, and with hard work and dedication, you can achieve your dream career as an AI developer.

Becoming an AI developer offers endless opportunities to take on intellectual challenges, help create innovative solutions, and have a fulfilling career. With the increasing demand for AI professionals, there is no better time to start pursuing your passion for AI development.

FAQ’S

What is an AI developer?

An AI developer is a professional who designs, builds, and maintains artificial intelligence (AI) applications. These applications can range from chatbots and virtual assistants to complex deep-learning models that require large amounts of data to function.

What skills are required to become an AI developer?

To become an AI developer, you need to have a strong foundation in programming languages such as Python and Java, as well as knowledge of machine learning algorithms, statistical analysis, and data structures. It’s also important to have a solid understanding of data science concepts, such as data visualization, data mining, and data preprocessing.

What education is required to become an AI developer?

While there is no strict education requirement for becoming an AI developer, most professionals in this field have a bachelor’s or master’s degree in computer science, mathematics, statistics, or a related field. Additionally, many AI developers pursue additional training through online courses, boot camps, or other types of specialized training programs.

What are some common tools used by AI developers?

AI developers use a variety of tools to build and deploy their applications. Some of the most popular tools include Python libraries like TensorFlow and Keras, as well as programming frameworks like PyTorch and sci-kit-learn.