AI Engineering Tutorial
AI Engineering is an interdisciplinary field that focuses on the design, development, testing, deployment, and monitoring of artificial intelligence systems. AI Engineering enables us to develop AI solutions for real world problems. It has a wide range of applications in natural language processing, computer vision, healthcare, robotics etc.
This AI Engineering tutorial provides a good understanding of AI engineering fundamentals and core concepts. This tutorial is designed for beginners as well as for working professionals who are interested in building a career in artificial intelligence engineering. It also provides all resources required to master artificial intelligence engineering, such as machine learning, deep learning, computer vision, NLP, etc.
What is AI?
Artificial intelligence is a technology that enables computers and machines to perform tasks (simulations) that are usually done by humans, such as problem-solving, decision-making, reasoning, learning, etc.
What is AI Engineering?
AI engineering is a field of study that focuses on developing intelligent systems that enable AI to be applied in the real world. It is an interdisciplinary engineering field that combines computer science, software engineering, and mathematics to create intelligent systems. It involves developing and implementing AI solutions, such as machine learning algorithms, natural language processing systems, and computer vision systems.
Who is an AI Engineer?
An AI engineer is a software engineer who works on the design, development, testing, and deployment of AI software. An AI engineer uses different types of software tools and frameworks to develop and deploy an AI software or system.
Why Learn AI Engineering?
With the development of large language models (LLMs), AI is again gaining popularity, and every sector is willing to utilize its power.
AI has a range of applications, including healthcare, natural language processing, computer vision, robotics, etc. As companies are using AI to automate repetitive tasks, there are huge opportunities for AI engineers.
AI Engineering Jobs & Career Opportunities
With advancements in generative AI, companies are in a race to adopt AI to automate their repetitive tasks. This has opened a vast opportunity for individuals with strong AI engineering skills. There are many AI specialization opportunities for any AI engineering learner. Some of them are listed below −
- AI Engineer
- Machine Learning Engineer
- AI Research Scientist
- AI/ ML DevOps
- GenAI Engineer
- Data Scientist
- AI Developer
- AI Product Manager
- Computer Vision Engineer
- AI Ethicist
- AI Algorithm Developer
- Many more..
Programming Languages for AI Engineering
Many programming languages, such as Python, Java, Scala, C++, R, Lisp, etc., are currently being used for AI engineering tasks, but Python is the most popular choice for AI. Python provides easy prototyping for AI software. It is robust and provides many features suitable for AI engineering.
Tools and Frameworks for AI Engineering
Tools and frameworks make AI development easy and hassle-free. There are many tools and frameworks that you can apply to your AI projects. Some are as follows −
- TensorFlow
- Keras
- PyTorch
- Scikit-learn
- LangChain
- Hugging Face
- OpenNN
- OpenAI
- DL4J
- CNTK
- Apache MXnet
Data Science Vs AI Engineering
Data science and AI Engineering go hand in hand, but there are also some differences. Data science mainly focuses on data handling, preparation, model development, etc., whereas AI engineering focuses on software development for AI solutions. AI engineering uses data science techniques to build solutions for real-world problems.
Applications of AI Engineering
AI engineering has a wide range of applications in each sector. Nowadays, each company uses AI and allied services to enhance productivity. The following are some areas where AI engineering is broadly used −
- Natural language processing (NLP)
- Computer Vision
- Healthcare
- Robotics
- Customer services
- Finance
- Marketing
Who Should Learn AI Engineering?
This tutorial is prepared for beginners as well as working professionals to help them understand basic to advanced concepts of artificial intelligence engineering. It covers most of the important concepts related to master AI engineering.
This tutorial is helpful for students aiming to build their career in AI and Data Science. It is also helpful for professionals aiming to enhance their skills or entrepreneurs trying to integrate AI with their business.
Prerequisites to Learn AI Engineering
Before proceeding with this tutorial, you need to have a basic knowledge of the following −
- Python Programming Language − In this tutorial, we will be using Python for implementing the example programs. Having a good understanding of the basics of Python is required for full benefit of this tutorial.
- Basic Statistics and Mathematics − Although we have provided basic concepts whenever required, you are advised to have basic concepts of mathematics and statistics. It will help understand the tutorial at a greater level.
- Data Processing and Visualization Techniques − Data processing and visualization are essential parts of any AI engineering project. Having a good understanding of these techniques are essential to understand the tutorial at an ease level. There are many Python libraries and tools such as Pandas, NumPy, Matplotlib, etc. that can be used for data visualization and preparation.
- AI Tools and Frameworks − The AI frameworks are not essentially required to go through this tutorial, but using a framework will benefit you to complete any AI project with less time.
Getting Started with AI Engineering
Getting started to learn AI Engineering involves a few steps, which helps build a solid foundation. The following is a brief step-by-step guide to get started with AI Engineering −
- Learn Mathematics, Programming, and Data Manipulation − Learn the basics of mathematics, statistics, any programming language such as Python, R, Java, Scla, etc., and data manipulation and visualization techniques. These are basic building blocks to learn AI engineering effectively.
- Learn AI and ML Fundamentals − Next is to learn the fundamental concepts of artificial intelligence and machine learning. Learn machine learning algorithms - supervised, unsupervised, reinforcement.
- Choose AI Tools and Frameworks − There are many AI tools and frameworks that will help you start an AI engineering journey at an advanced level. We recommend learning TensorFlow or PyTorch, Keras, Scikit-learn, etc.
- Learn Specialized AI Techniques − There are more specialized AI techniques that you require to learn AI engineering at an advanced level. Deep Learning, NLP and Computer Vision, Generative AI, etc. are some of the advanced techniques to learn.
- Develop AI Applications with Real Data − Start working on developing the projects that solve some real world problems.
- Professional Development − Participate in AI/ML communities like GitHub and develop a professional portfolio and engage actively on professional platforms like LinkedIn.
FAQs on AI Engineering
There are some frequently asked questions (FAQs) about AI Engineering. This section tries to answer them briefly.
AI Engineering is a field of study that focuses on building autonomous systems. It includes planning, designing, developing, testing, developing, and monitoring an AI software or system.
Programming language, basics of mathematics and statistics, data handling and visualization, machine learning and AI algorithms, Deep learning, NLP, computer vision, AI deployment and DevOps, AI/ml tools and frameworks.
NLP, Computer Vision, Robotics, Healthcare, Finance, marketing, etc.
The following types of AI algorithms are used in AI engineering −
- Supervised Learning − regression, classification
- Unsupervised Learning − clustering, dimensionality reduction
- Reinforcement learning − Q-learning
Data quality, model complexity, bias in AI, data privacy and security, are some challenges faced by an AI engineer.
General Artificial intelligence, AI for Good governance, AI for social good, explainable AI, etc.
We provide the complete AI engineering resources. We have published all the supporting tutorials on our website. We also offer video tutorials on our marketplace.