Perfect Roadmap To Become AI Engineers In 2024 With Free Videos And Materials

Hello all, my name is Krish Naik and welcome to my YouTube channel!

Many people have been requesting an AI engineer roadmap. I always aim to provide roadmaps with free videos, complete courses, documentation, end-to-end projects, and everything you need to succeed. Today, I am introducing the AI Engineer Roadmap for 2024. This post will cover everything you need to become an AI engineer, along with all the free resources you'll require.

Understanding the Role of an AI Engineer

Before diving into the roadmap, it is crucial to understand what an AI engineer does. You can find detailed videos explaining this role intricately.

AI Engineer Job Description

Start by understanding the job descriptions. Most roles like data scientists, AI engineers, and machine learning engineers often overlap. It's beneficial to look at job descriptions from larger product-based companies to grasp the skills and responsibilities required.

• Focus on building strong relationships and collaboration.
• Understand roles like product management, quality engineering, and UX design.

Programming Language: Python

Python is indispensable for AI engineers. My channel hosts comprehensive playlists on Python programming, which cover topics from basic to intermediate concepts, data structures, and even project implementation using frameworks like Flask.

• Playlist Links: [Python Playlist in English] and [Python Playlist in Hindi]
• Outcome: Basic to intermediate Python skills, knowledge of data structures, ability to perform data analysis, and project creation.

Statistics

Statistics is vital whether you are a data scientist, analyst, or AI engineer. Detailed videos on my channel cover descriptive and inferential statistics with practical implementations.

• Links to supplementary resources like Khan Academy for advanced topics such as linear algebra and differential equations.

Exploratory Data Analysis (EDA) and Feature Engineering

Effective data analysis requires skills in EDA and feature engineering. Detailed playlists and live sessions are available on my channel for both topics.

Databases

Knowledge of both SQL and NoSQL databases is recommended. My videos explain database integration with Python, data insertion, and other essential functions.

• SQL Databases: MySQL, Apache Cassandra
• NoSQL Databases: MongoDB

Machine Learning

The machine learning section includes supervised and unsupervised algorithms, practical implementation, and mathematical intuition. Playlists are available in both English and Hindi languages.

• Focus on algorithms like Linear Regression, Decision Trees, Random Forest, and various ensemble methods.

Deep Learning

My channel covers extensive deep learning topics including CNN, RNN, Transformers, and even modern architectures like BERT.

Deploying Machine Learning Models

After mastering machine learning and deep learning, the next step is deployment. Frameworks like Flask, Gradio, BentoML, MLflow, and DVC will help you bring your models to production.

Machine Learning Operations (MLOps)

MLOps integrates model deployment and life-cycle management. Understanding cloud platforms like AWS, Azure, and GCP along with Docker, Kubernetes and CI/CD pipelines is crucial.

Big Data and Cloud Engineering

It’s beneficial to understand Big Data and Cloud Engineering as AI projects often involve communicating with data engineering and cloud teams. It helps in integrating data from various sources and fine-tuning models.

Generative AI

The roadmap also covers generative AI, which is becoming increasingly popular. Playlists on LangChain, LLM fine-tuning, and other related topics are available.

Additional Skills

• An understanding of Big Data Engineering and Cloud Engineering is a plus.
• Good communication skills to coordinate with various teams including product and iOT teams.

Internships and Continuous Learning

Once you've mastered these skills, apply for internships and continue learning. The landscape of AI is always evolving, and staying updated is crucial.

Conclusion

Following this roadmap will set you on the path to becoming an AI engineer in 2024. Consistent learning and commitment are essential. Explore all the free resources and videos provided to build a strong foundation. If you dedicate 3 to 4 hours daily, nothing can stop you from succeeding in this field.

Watch the roadmap video for a visual and detailed guide: