AI Engineer : The ULTIMATE Roadmap for 2024
Summary
TLDRThis 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
🤖 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.
📚 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.
Mindmap
Keywords
💡AI
💡Coding
💡Machine Learning
💡Python
💡TensorFlow
💡GPU
💡Cloud-based Solutions
💡API
💡Data Sets
💡Open Source
💡GitHub
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
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