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.

Outlines

00:00

🤖 AI Developer Roadmap Overview

This paragraph introduces the video's purpose, which is to provide a roadmap for becoming an AI developer. It addresses three types of viewers: those with coding knowledge interested in AI, those feeling FOMO about AI development, and the curious seeking knowledge. The speaker promises a clear perspective on entering the AI field, based on insights from top engineers with decades of experience. The video offers a straightforward guide to understanding AI development, starting with prerequisites such as coding knowledge and basic machine learning skills.

05:00

📚 Prerequisites and Tools for AI Development

The paragraph outlines the prerequisites for AI development, emphasizing the need for coding knowledge, specifically in Python, due to its simplicity and the availability of AI libraries. It also highlights the importance of a strong foundation in mathematics, including statistics, probability, and linear algebra, as well as understanding data structures and algorithms. The speaker recommends free online courses and resources for learning these skills. Additionally, the paragraph discusses the hardware and software requirements for AI development, such as a powerful CPU, GPU, and storage, and suggests using cloud-based solutions for computational power. It also mentions the importance of a code editor, collaboration platforms like GitHub, and data analysis tools for efficient workflow.

🔧 Building AI Models and Utilizing APIs

This paragraph delves into the practical aspects of AI development, such as building, training models, or combining existing models to create composite applications. It advises against starting from scratch and instead encourages leveraging open-source models and years of research. The paragraph introduces APIs as essential tools for interacting with pre-existing AI models, allowing developers to use their functionality without understanding the complex details of their construction or training. It also emphasizes the importance of data sets for training AI models and suggests sources for finding various types of data sets, such as text, images, audio, and video.

Keywords

💡AI

AI, or Artificial Intelligence, refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is the central theme, with the focus on how individuals can become AI developers and contribute to the rapidly growing field of generative AI.

💡Coding

Coding is the process of writing instructions in a programming language to create software or applications. The script emphasizes that having coding knowledge is a prerequisite for those looking to enter the AI field, as it is the foundation for developing AI models and applications.

💡Machine Learning

Machine Learning is a subset of AI that enables machines to learn and improve from experience without being explicitly programmed. The script mentions basic machine learning skills as a prerequisite for AI development, highlighting its importance in understanding and building AI systems.

💡Python

Python is a high-level programming language known for its readability and efficiency, making it the recommended language for AI development due to its simplicity and the availability of AI and machine learning libraries. The video script suggests learning Python as a key step in becoming an AI developer.

💡TensorFlow

TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks, ideal for machine learning and deep learning applications. It is mentioned in the script as one of the numerous AI and machine learning libraries available for Python.

💡GPU

A GPU, or Graphics Processing Unit, is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. The script highlights the importance of a powerful GPU for AI development due to the computational power required for training models.

💡Cloud-based Solutions

Cloud-based solutions refer to services delivered over the internet, which in the context of the video, provide the necessary computational power for AI development without the need for high-end hardware. Examples given in the script include Google Colab, AWS Sagemaker, and NVIDIA NeMo.

💡API

An API, or Application Programming Interface, is a set of protocols and tools for building software applications, allowing different systems to communicate with each other. The script explains that APIs are crucial for interacting with pre-existing AI models and using their functionality without understanding their complex details.

💡Data Sets

Data sets are collections of data used for training AI models. They can include text, images, audio, video, or code files, depending on the type of AI application being developed. The video script underscores the importance of data sets in AI development and provides resources for finding them.

💡Open Source

Open source refers to something that can be modified because its design is publicly accessible. In the context of the video, open source projects and communities are recommended as valuable resources for AI developers to learn, contribute, and build upon existing work.

💡GitHub

GitHub is a platform for version control and collaboration that allows developers to work together on projects. The script mentions GitHub as a critical platform for AI developers to collaborate on projects and contribute to the open-source community.

Highlights

The video aims to provide a roadmap for becoming an AI developer, targeting three categories of people: those with coding knowledge and AI passion, those feeling FOMO about AI development, and those simply curious about AI domains.

Coding knowledge and basic machine learning skills are prerequisites for getting into AI development.

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

Online platforms like Codecademy, Coursera, Udemy, and YouTube offer courses to learn Python.

A strong foundation in mathematics, especially statistics, probability, and linear algebra, is essential for AI development.

Resources like Khan Academy and MIT OpenCourseWare provide free mathematical foundation building.

Understanding data structures and algorithms is fundamental for efficient programming.

The 'Making Friends with Machine Learning' course by Kazzy Kosukov is recommended for beginners in AI.

High computational power is needed for AI, with recommendations for a high-end CPU, GPU, and storage.

Cloud-based solutions like Google Colab and AWS Sagemaker can be used instead of investing in high-end hardware initially.

Linux is often preferred for AI development due to its flexibility and command line interface.

A code editor like Sublime or VS Code is essential for AI development, with cloud-based options like Replit also available.

GitHub is a platform for collaboration, and tools like Jupyter Notebooks or the Pandas library can speed up data analysis workflows.

Generative AI developers typically build, train models, or combine existing models to create composite applications.

Open source models and APIs like OpenAI and Stable Diffusion can be used to leverage pre-existing AI functionalities.

Datasets are crucial for training AI models and can be found on platforms like Kaggle and the UCI Machine Learning Repository.

Communities and open-source projects on GitHub offer opportunities to contribute and learn from others in the field.

Self-starter projects like creating a chatbot or a recommendation system are suggested to test and develop AI skills.

The AI job market is robust and growing, with an expected annual growth rate of 37.3% between 2023 and 2030.

Businesses are actively hiring AI-related positions, indicating a strong demand for AI developers.

The video promises an exciting announcement related to leveling up as a '100x engineer' for those interested in AI development.

Transcripts

if you clicked on this video you belong

to one of three categories of people one

you know some coding and you're

passionate about Ai and you're looking

to get into the scene or make a career

out of it or two there's a lot of fomo

you're feeling about getting into AI as

a Dev and you don't know what to do

about it or three You're simply curious

about the domains of AI and just here to

gain some knowledge regardless of who we

are and which category you belong to I'm

sure you will find some value when you

watch this video Until the End

presenting the ultimate roadmap to

becoming an AI developer there are

plenty of videos out there on the

internet but none of them give a clear

point perspective on how you actually

get into this so me and my team spent a

few days with some of the top engineers

in the country the folks who've been

Developers for over decades and have

been actively working on generate AI

projects and one of them also sold as

previous company to an academy after

getting all the Intel from them I've

made into a format that would be simple

and straightforward for you guys to

understand and follow let's dive in but

before that hit the Subscribe button now

on to the roadmap number one the

prerequisites before foreign you need to

have some coding knowledge and basic

machine learning skills it's like before

riding a bike you need to know how to

balance yourself on a bicycle there are

three things you need to know before

getting started with AI number one

programming language python is the most

recommended language for AI development

just because of its Simplicity and the

availability of numerous Ai and machine

learning libraries like tensorflow

pytorch and scikit-learn python can be

learned from many online platforms such

as code academy Coursera or udemy or

even YouTube here are some of their top

free courses next step is the basic

concept before diving into AI having a

good grasp of mathematics especially

statistics probability and linear

algebra is essential you also need to

learn about data structures and

algorithms which are the fundamentals of

efficient programming websites like Khan

Academy or MIT open courseware will

provide you free resources to build your

mathematical foundation and platforms

like lead code and hacker rank will help

you understanding data structures and

algorithms next is the machine learning

course once you take the first two

points we recommend the course making

friends with machine learning by kazzy

kosukov which is a freely available ml

course on YouTube it's the best one to

start with once you've covered these

three prerequisites you can get into the

actual AI stuff now ai takes a lot of

computational power so it's critical for

you to have a really good processor a

high-end CPU say an i7 or an AMD ryzen 7

is a good starting point 16 gigs of RAM

is pretty reasonable for early

development and crucially you need a

powerful GPU like the RTX 3060 3070 and

if you can afford the 40 series great

make sure there's a good amount of

storage a 1tb SSD is a good start and

finally an efficient cooling system

however you don't have to invest in this

high-end Hardware up front you can use

cloud-based Solutions like Google collab

aw sagemaker Nvidia Nemo llm cloud

service hugging face interference

endpoints Mosaic ml interference and

paper space these cloud-based tools

although much less efficient than having

GPU inside your PC provide you with the

necessary computational power to start

out now let's move on to this software

any modern OS like Windows Linux or Mac

OS can be used for AI development but

Linux is often preferred for its

flexibility and command line interface a

critical piece of software you'll need

as a developer is obviously a code

editor like sublime or vs code however

you can also use cloud-based

coordinators like replit it's one of the

best ones out there you don't even need

a computer to start with you can write

code from even your iPad or tablet

they've recently introduced a

Ghostwriter an AI code assistant similar

to github's co-pilot think of it as an

AI assistant that writes code for you

since you'll be working with multiple

developers on this project you need a

platform to collaborate on and for that

you have GitHub you'll also need a data

analysis tool like jupyter notebooks or

pandas library in order to make your

workflow faster these tools help you

avoid tedious tasks of downloading last

data set files and let you do the data

analysis on the cloud alright so we have

the knowledge hardware and software set

what do we do now see on a high level a

generative AI developer does one of

these things build a model train a model

or stitch together existing models to

create it's something composite out of

them there are plenty of Open Source

models out there for developers to use

so building a model from scratch may not

be the smartest thing to do especially

if you're new to all this you don't want

to build on top of what has already been

built with years of research it's one of

the Privileges of living in 2023 let's

look at the two main ingredients to

start building on top next we have apis

for those of you don't know an API is

like a bridge between two systems it's a

messenger that communicates between two

different programs the openai API and

the stable diffusion API are great

places to start learning how to interact

with pre-existing AI models apis allow

you to use the functionality of these

models without having to delve into

their complex details of their

construction or training to learn how to

use these you can check out their API

documentation every product or service

that has an API makes a document that

guides you on how to use their apis and

they're pretty easy to understand next

we have data sets data sets are

fundamental for training your AI models

they're basically huge files with a lot

of information it can be text images

audio video code files so anything

depending on what kind of training data

you are looking for for example chat CPU

was trained on a huge data set

consisting vast amounts of text Data

like books articles blog posts websites

Etc mid-journey was trained on huge

amounts of image data so it can learn

what a cat looks like or what a human

being looks like depending on the nature

of your projects different data sets

will be required websites like kaggle

UCI machine learning repository and

Google's data set search are great

places to find data sets for a variety

of applications like I told you the hard

work has already been done by some smart

people your job is just Stitch these

things together to make something of

value out of them you now have all the

tools and knowledge needed to become an

AI developer but how exactly can you

start applying this where do you even

start applying this that brings me to my

final Point Community is an open source

projects some of the best communities I

have been recommended are open data

science data Science Central global data

science forum and subreddits like R

machine learning and r slash Artificial

Intelligence coming to open source

projects GitHub is a fantastic place to

get involved with projects like Dali

mini hugging face Transformers and deep

face live offering exciting

opportunities to contribute and learn

some of the devs I spoke to strongly

recommended hugging face in order to

find cool open source projects even

participating in discussions on

platforms like stack Overflow Reddit and

AI related forums can provide valuable

insight and experience and there you

have it the ultimate road map to

becoming a generative AI developer after

acquiring the skills and knowledge to

become an AI developer you can start

working on self-starter projects like

creating a chat bot building a

recommendation system or even developing

a facial recognition system these are

fun projects to embark on to test your

skills and develop your knowledge the

job market for AI delvers is robust and

growing according to Forbes AI is

expected to see an annual growth rate of

37.3 percent between 2023 and 2030.

according to a McKinsey report in 2022

39 of businesses reported hiring

software engineers and 35 hired data

Engineers for AI related positions this

feel is not slowing down anytime soon

and it's time for you to consider

leveling up as a 100x engineer we're

going to announce something exciting

very soon so check the link in the

description and don't forget to

subscribe