AI Engineer : The ULTIMATE Roadmap for 2024

Summary

This video offers an ultimate roadmap for aspiring AI developers, addressing three key groups: those with coding skills looking to delve into AI, individuals experiencing FOMO about AI development, and the curious seeking knowledge. It outlines prerequisites like coding and machine learning skills, suggests learning resources, and emphasizes the importance of computational power and software tools. The script guides viewers on leveraging APIs, datasets, and community involvement to build AI models, highlighting the robust job market and growth opportunities in the field.

Takeaways

• 👨‍💻 **Coding Passion**: The video is aimed at those passionate about AI and looking to make a career in it.
• 🤔 **FOMO for AI**: It addresses those feeling the fear of missing out on AI development opportunities.
• 👀 **Curiosity Driven**: The video is also for those who are simply curious about AI and want to gain knowledge.
• 🛠️ **Prerequisites**: Before diving into AI, one must have coding knowledge and basic machine learning skills.
• 🐍 **Python Programming**: Python is the recommended language for AI development due to its simplicity and available libraries.
• 📚 **Mathematical Foundation**: A strong grasp of mathematics, including statistics, probability, and linear algebra, is essential.
• 💻 **Hardware Requirements**: High computational power is needed for AI, including a powerful CPU, GPU, and sufficient storage.
• ☁️ **Cloud-based Solutions**: For those who can't invest in high-end hardware, cloud-based tools offer necessary computational power.
• 🔧 **Software Tools**: Linux is often preferred for AI development, and tools like code editors and GitHub are essential for collaboration.
• 🔍 **Data Analysis**: Tools like Jupyter notebooks and the pandas library are crucial for efficient data analysis workflows.
• 🏗️ **Building AI**: Generative AI developers typically build, train models, or combine existing models to create composite applications.
• 🔗 **APIs and Datasets**: APIs bridge systems and datasets are fundamental for training AI models, with resources available online.
• 👥 **Community and Collaboration**: Engaging with communities and contributing to open-source projects is key for learning and growth.
• 🚀 **Career Growth**: The AI job market is robust and growing, with significant opportunities for those looking to level up as engineers.

Q & A

What are the three categories of people interested in AI development according to the video?

The three categories are: 1) People who know some coding and are passionate about AI, looking to make a career out of it. 2) People feeling FOMO about AI development and unsure about how to get started. 3) People who are simply curious about AI and want to gain knowledge.

What are the prerequisites for becoming an AI developer as mentioned in the video?

The prerequisites include having coding knowledge, basic machine learning skills, understanding of programming language Python, mathematics (especially statistics, probability, and linear algebra), and knowledge of data structures and algorithms.

Which programming language is most recommended for AI development?

Python is the most recommended language for AI development due to its simplicity and the availability of numerous AI and machine learning libraries.

What are some online platforms where Python can be learned for AI development?

Python can be learned from online platforms such as Codecademy, Coursera, Udemy, or even YouTube.

What is the importance of having a good grasp of mathematics in AI development?

Having a good grasp of mathematics, especially statistics, probability, and linear algebra, is essential for understanding and implementing AI algorithms and models.

What is the recommended course for beginners to start learning machine learning?

The recommended course for beginners is 'Making Friends with Machine Learning' by Kazi Z. Kosukov, which is freely available on YouTube.

What are the hardware requirements for AI development?

The hardware requirements include a high-end CPU (like i7 or AMD Ryzen 7), 16GB of RAM, a powerful GPU (like RTX 3060, 3070, or 40 series), a 1TB SSD for storage, and an efficient cooling system.

Can cloud-based solutions be used instead of investing in high-end hardware for AI development?

Yes, cloud-based solutions like Google Colab, AWS Sagemaker, Nvidia NeMo, Hugging Face Inference, Mosaic ML Inference, and Paperspace can provide the necessary computational power to start AI development without investing in high-end hardware.

What are some of the software tools needed for AI development?

Some necessary software tools include a modern OS (Windows, Linux, or macOS), a code editor (like Sublime or VS Code), cloud-based IDEs (like Replit), GitHub for collaboration, and data analysis tools like Jupyter Notebooks or the pandas library.

What are the two main activities a generative AI developer typically engages in?

A generative AI developer typically engages in building a model, training a model, or stitching together existing models to create a composite solution.

What role do APIs play in AI development, and how can one learn to use them?

APIs act as a bridge between two systems, allowing communication between different programs. They enable the use of pre-existing AI models without delving into their complex construction or training details. One can learn to use APIs by checking out their documentation, which is provided by every product or service that offers an API.

What are data sets, and why are they important for AI development?

Data sets are fundamental for training AI models; they are large files containing a lot of information, which can be text, images, audio, video, or code files, depending on the training data needed. They are important because they provide the raw material that AI models learn from.

Where can one find data sets for AI model training?

Data sets can be found on websites like Kaggle, the UCI Machine Learning Repository, and Google's Dataset Search, which offer a variety of data sets for different applications.

What are some ways to get involved in the AI community and contribute to open source projects?

One can get involved in the AI community through platforms like Open Data Science, Data Science Central, Global Data Science Forum, and subreddits like r/MachineLearning and r/ArtificialIntelligence. Contributing to open source projects on GitHub, such as Dali, Hugging Face Transformers, and DeepFace, offers exciting opportunities to learn and contribute.

What self-starter projects are suggested for new AI developers to test their skills?

New AI developers can work on projects like creating a chatbot, building a recommendation system, or developing a facial recognition system to test their skills and develop their knowledge.

What is the projected growth rate for the AI job market according to Forbes?

According to Forbes, the AI job market is expected to see an annual growth rate of 37.3 percent between 2023 and 2030.

What did a McKinsey report in 2022 reveal about hiring trends for AI-related positions?

The McKinsey report in 2022 revealed that 39% of businesses reported hiring software engineers and 35% hired data engineers for AI-related positions, indicating a strong demand in the job market.