Software Development

How to Become AI Software Developers

Want to Build the Future? Become an AI Software Developer

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Artificial intelligence, or AI, enables machines to think and learn the way humans do. Though it has been in the market since the last decade, it gained remarkable popularity in 2021 when ChatGPT was launched.

Currently, the market for AI is surging at a CAGR of 28.46%, leading to an overall volume of $826.70 billion by 2030.

Thanks to the rising numbers, the technology has raised the need for AI features in almost every software today. Connecting to this, the need for relevant AI skills for developers has also surged, leading to a significant increase in the demand for AI developers.

Read ahead in this blog and learn how to become an AI developer!

Understanding the Basics of Artificial Intelligence

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Artificial intelligence is a broader term that encapsulates several other concepts in it. Here are some of the crucial ones!

  • Machine learning is a part of AI that is aimed at developing algorithms trained to analyze vast datasets to identify and analyze patterns. These algorithms improve with time in terms of performance.
  • Deep learning is the subset of AI that harnesses the power of neural networks to teach computers to process data like the human brain. It can be fed with data, such as images, text, speech, etc.
  • A neural network is a comprehensive network of interconnected nodes or neurons that process and learn from the data, just like a human brain. Deep learning consists of a layer of neural networks.

Now, here is an abstract of these concepts!

AI enables computers to think and learn as humans do. Machine learning includes the creation and training of algorithms meant to identify patterns in massive datasets. Data science is an encapsulation of AI, machine learning, and several other technologies and methods that help in making informed and better decisions based on comprehensive data analysis.

Required Educational Background and Skills

The process of how to become an AI developer requires professional qualifications as compared to regular development. The advent of generative AI has further fine-tuned the qualifications required.

  • Relevant degrees and certifications: To become a professional AI developer, it is not necessary to have a degree. However, if you have at least a bachelor’s in computer science, statistics, AI, or data science, you can bag a great job.
  • Essential programming languages: On top of professional qualifications, proficiency in programming languages is also favorable. You have to be proficient in Python, along with relevant knowledge about Julia, R, Java, and Javascript.
  • Math and statistics knowledge: Mathematics knowledge about linear algebra, probability, statistics, and calculus can help in the data-cleaning process.

Familiarize Yourself with AI Development Tools and Libraries

When learning about scripting languages, there are several AI tools, frameworks, and libraries that come in handy. These libraries make it easy for the developer to collect, clean, and analyze data. Let’s highlight some of those tools!

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Popular AI Frameworks Data Analysis and Visualization Tools
TensorFlow
  • Open-source deep learning framework.
  • Ideal for developing and deploying ML/DL models.
  • Fit for dealing with large unstructured datasets.
Pandas
  • Open-source Python development library.
  • Used for analyzing, cleaning, exploring, and manipulating large datasets.
PyTorch
  • Open-source library developed by Meta.
  • Ideal for faster and easier deep-learning model development.
Matplotlib
  • Low-level graphic plotting Python library.
  • Handy for creating static, animated, and interactive visualizations.
Keras
  • Open-source Python library.
  • Ideal for configuring and testing neural networks.

TensorFlow

Popular AI Frameworks
  • Open-source deep learning framework.
  • Ideal for developing and deploying ML/DL models.
  • Fit for dealing with large unstructured datasets.

PyTorch

Popular AI Frameworks
  • The open-source library developed by Meta.
  • Ideal for faster and easy deep-learning model development.

Keras

Popular AI Frameworks
  • Open-source Python library.
  • It is ideal to configure and test neural networks.

Pandas

Data Analysis and Visualization Tools
  • It is an open-sourcePython development library.
  • It is used in analyzing, cleaning, exploring, and manipulating large datasets.

Matplotlib

Data Analysis and Visualization Tools
  • A low-level graphic plotting Python library.
  • It comes in handy in creating static, animated, and interactive visualizations.

Learning Data Structures and Algorithms

An extensive knowledge of DSA, or Data Structures and Algorithms, is a pivotal element in how to become an AI Developer. An AI developer has to deal with data storage, data retrieval, data cleaning, sorting, and searching in the process of creating AI applications.

Therefore, it is critical to know about data structures like hash tables, trees, graphs, dynamic arrays, linked lists, etc.

Besides, an AI developer should also have a thorough understanding of AI and ML algorithms. Based on the categories, several algorithms are used in AI. The primary difference between them is the way data is acquired and labeled. Furthermore, some algorithms are also differentiated based on the way they are trained.

  • Supervised learning: Supervised learning is a scenario where labeled data is fed to the algorithm to learn and improve with time. Examples of supervised learning are decision trees, random forests, SVM, linear regression, logistic regression, etc.
  • Unsupervised learning: Unlike supervised learning, here, the data fed to the algorithm is unlabeled, which is used to build models to establish relationships between different data points. Examples of unsupervised learning are clustering and the Gaussian mixture model.
  • Reinforcement learning: Reinforcement learning is the one where the algorithm learns from the feedback received from the result of its action. Some great examples of reinforcement models are model-based, policy-based, and value-based.

Overview of some algorithms falling under the categories!

  1. Decision trees: It is a type of supervised learning algorithm that uses root data and ASM selection criteria to classify data.
  2. SVM: Support vector machine is an AI algorithm used for classification and regression. It includes plotting each piece of data on an N-dimensional plane.
  3. Random forest: Random forest is a collection of different decision trees that are used to gain more accurate results.
  4. Neural networks: Neural networks are complex AI algorithms that are classified under both supervised and unsupervised algorithms. Hence, they are used for data classification, regression, pattern recognition, and more.

Exploring Deep Learning and Neural Networks

As an AI developer, you have to deal with different types of data, such as images, sounds, text, etc., to capture relevant insights from it. And the technology that needs to be practiced for this is deep learning.

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Deep learning is an essential part of being an AI developer. It harnesses the power of artificial neural networks to identify patterns for accurate image recognition, speech recognition, and natural language processing. There are three types of deep learning models that are commonly used, such as

  • CNN: Convolutional Neural Networks, or CNNs, are the ones that are used for image recognition. These models can easily sort out objects even if they are obscured as well as distorted.
  • GANs: Generative Adversarial Networks are part of generative AI development that use two neural networks to generate artificial yet authentic new data based on the provided training data. This data can be an image, text, video, etc. ChatGPT and Gemini are the perfect examples of the same.
  • RNN: Recurring Neural Networks are used for natural language processing and speech recognition. These are ideal for understanding the context of the phrase or sentence.

Besides these models, deep learning has a term named backpropagation.

Backpropagation is an ML technique that uses the gradient descent algorithm to make changes the weights and biases of a neural network to improve its overall accuracy.

Understanding Natural Language Processing (NLP)

NLP is a part of AI where machines are trained to understand human language and its complexities. A plethora of statistical and machine-learning methods are used that help in understanding relationships between words, letters, and sentences. It can be in the written format or verbal format.

NLP has tremendous applications in the AI field, including

  • Chatbots
  • Voice assistants
  • Language translators
  • Sentiment analysis
  • Grammar checkers

When you’re on the journey of becoming an AI developer, having a solid understanding of Natural Language Processing (NLP) is essential. Mastering NLP tools, libraries, and frameworks is crucial for building and deploying effective AI solutions. Here are some key AI tools for software developers to master for NLP:

Essential Libraries for NLP

  • Natural Language Toolkit or NLTK: Supports all the essential functions of NLP.
  • Genism: To identify semantic similarities
  • TextBlob: Learn the basic NLP tasks
  • spaCy: Get the fastest syntactic parser.
  • Scikit-learn: Access to algorithms for ML models
  • Hugging Face transformer: Get access to over 20,000 pre-trained models

Working on Real-World AI Projects

Working on real-world projects is a way to boost the confidence of a budding AI developer. Furthermore, involving yourself in projects improves creativity, problem-solving skills, teamwork skills, etc. In fact, it is one of the key requirements when you go for getting a job.

Here are some ways to get hands-on experience in AI project development.

  • Participate in hackathons and competitions to gain skills, learn from experts, and boost your network.
  • Internships are another way to enhance your AI skills while working in a professional environment alongside people adept in your field.
  • Online communities are the places where you can meet AI experts from around the world. You can collaborate on the projects and get professional help with anything related to your project.
  • Lastly, you can take online courses or start your personal project to learn and advance your skills.

Ethics in AI Development

While the applications of AI are endless and alluring, there are some areas where AI may not be ethical.

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  1. Bias: If the AI system is trained on a skewed dataset or has a flaw in its algorithm, it may exhibit biased results. For example, if an AI model for hiring is trained based on a skewed dataset, such as favoring a certain demographic, it can lead to discrimination.
  2. Transparency: Most of the AI systems operate as Black Box, which means you cannot know how the outcomes are reached. This hides the working of the AI and raises objections regarding its fairness, and reliability.
  3. Privacy: Data is the heart of every AI algorithm. As cyberattacks are pretty common, there is always a risk of data compromise, especially in the retail and eCommerce sector, which raises privacy concerns.

These ethical concerns can be fixed by creating ethical AI systems. It can be done via the following methods!

  • Data Governance and Regulatory Oversight, such as GDPR, CCPA, etc.
  • Privacy-preserving technologies, including federated learning and differential privacy.
  • Algorithmic Auditing and Oversight
  • Diverse and Representative Data Collection, along with techniques like data augmentation, bias detection, and algorithmic fairness.
  • Algorithmic Fairness and Explainability through fairness-aware learning and model explainability.

Staying Updated with AI Trends and Developments

The process of how to become an AI developer is a never-ending one. Why? Well, AI is the technology of the future, and the advent of generative AI has further strengthened its roots. Every business is using AI in one form or the other. As per stats, around 77% of companies are either using or exploring the utility of AI in their businesses.

Therefore, if you want to stay a good AI developer for a long time, it is crucial to stay updated with the current and futuristic AI trends.

Keeping up with the latest AI trends like ethical AI, explainable AI, automated AI development, etc., can help developers bag better jobs and stay updated with the dynamic market.

The sources from where the developers can keep themselves updated are

  • Developer communities
  • Blogs
  • Courses
  • Research papers
  • Conferences and events

Building a Portfolio and Networking

An impressive portfolio is the best asset in the process of how to become an AI developer. Therefore, you need to design a good one! Here are some tips to showcase your AI skills via the portfolio!
  1. Choose the right project with different algorithms and data structures.
  2. Provide detailed descriptions about project challenges, AI models created, models used, etc.
  3. Add visual elements like graphs, charts, and videos to elaborate the functionality of the project.
  4. Add code samples for reference.
Furthermore, invest time in networking over the web as it will provide you maximum exposure. Make accounts on online hiring forums, such as Indeed, LinkedIn, etc. Moreover, attend online meets and events to communicate with experts.

Career Path and Job Opportunities in AI

Well, we believe that the question of how to become an AI developer is answered well. Once you have acquired the skills of an AI developer, there are multiple job roles that you can take. For businesses looking to implement AI solutions, it’s often a good strategy to hire dedicated software developers with AI expertise. Here are some of the roles you can pursue as an AI developer!

  1. AI engineer
  2. Machine learning engineer
  3. Data scientist
  4. Business intelligence developer
  5. Computer vision engineer

And a lot more! There are several companies in almost every industry sector that hire AI developers, such as Google, Amazon, TCS, Accenture, Infosys, Wipro, and more.

Conclusion

So, these are the necessary steps, courses, skills, and qualifications about how to become an AI developer. As the application of AI is increasing day by day, the need for AI developers is also surging in the market. Make sure that you opt for the best certifications and courses from the right schools. Moreover, work with a professional AI firm for your internship, as there is a lot to learn.

It is best to learn from an expert. Therefore, you can get in touch with them now!

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Author:

Manan Jobanputra

Manan is a seasoned web developer with over 13 years of experience. He specializes in PHP, ReactJS, Angular, and WordPress, with a strong focus on healthcare industry compliance. His extensive expertise ensures top-notch, secure, and compliant web solutions tailored to industry standards.

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You're on the path to guaranteed success

We assure you that your investment in healthcare IT solutions will lead to tangible success, whether in improving patient care, streamlining operations, or advancing technological innovation within the industry