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

Summary

This video serves as a comprehensive AI Engineer roadmap, outlining essential skills and tools needed to enter the field of artificial intelligence. Krishak covers key areas such as programming in Python, statistics, machine learning, deep learning, NLP, and MLOps. He emphasizes the importance of understanding cloud and big data engineering, while also offering practical resources and project links. With guidance on deploying AI models and a focus on generative AI, the video aims to provide a clear path for beginners and aspiring AI engineers. Krishak also shares internship opportunities and encourages viewers to join his community for additional support.

Takeaways

• 😀 Understanding the AI Engineer Role: It's essential to first grasp what an AI Engineer does. This involves knowing the job description and the skills needed to succeed in the role.
• 😀 Importance of Python: Python is the recommended programming language for aspiring AI Engineers, as it is widely used in AI, Generative AI, and LLM development.
• 😀 Statistics Knowledge is Crucial: A solid understanding of statistics, both descriptive and inferential, is necessary for AI Engineers, regardless of whether they are working in Data Science or Machine Learning.
• 😀 Master Data Preparation: Skills in exploratory data analysis (EDA) and feature engineering are critical for manipulating and preparing data for AI projects.
• 😀 Learn Databases: Knowledge of both SQL (e.g., MySQL) and NoSQL (e.g., MongoDB, Cassandra) databases is essential for AI Engineers to handle and integrate large datasets.
• 😀 Machine Learning Fundamentals: Familiarity with both supervised and unsupervised machine learning algorithms (e.g., linear regression, decision trees, XGBoost) is necessary for AI Engineers.
• 😀 Deep Learning Expertise: AI Engineers should master deep learning techniques, including CNNs, RNNs, transformers, and attention mechanisms for building advanced AI models.
• 😀 Model Deployment and MLOps: Learning deployment frameworks like Flask, Gradio, and BentoML, as well as MLOps tools for CI/CD pipelines and model monitoring, is vital for production-ready AI systems.
• 😀 Generative AI Knowledge: With the rise of Generative AI, AI Engineers must be familiar with pre-trained models and fine-tuning techniques using tools like LangChain and AWS Bedrock.
• 😀 Gain Knowledge in Big Data and Cloud Engineering: Understanding Big Data ecosystems and cloud platforms (AWS, Azure, GCP) can be beneficial for AI Engineers working on large-scale AI projects, ensuring they collaborate effectively with other teams.

Q & A

What is the primary aim of Krishak's YouTube channel?

The primary aim of Krishak's YouTube channel is to democratize AI education by providing free resources and comprehensive roadmaps for aspiring AI engineers.

How does Krishak provide the AI engineer roadmap?

Krishak provides the AI engineer roadmap by offering free videos, courses, and documentation, ensuring that all materials are available in one place for easy access.

What is the first step in the AI engineer roadmap?

The first step in the AI engineer roadmap is to understand what an AI engineer does. This includes reviewing job descriptions and understanding the roles and responsibilities of an AI engineer.

What programming language does Krishak recommend for aspiring AI engineers?

Krishak recommends Python as the primary programming language for AI engineers, emphasizing its versatility and importance in AI and machine learning applications.

Why is learning statistics crucial for AI engineers?

Learning statistics is crucial for AI engineers because it forms the foundation for data analysis, model training, and decision-making processes in machine learning and AI projects.

What are the key skills needed to work with data as an AI engineer?

The key skills needed to work with data include proficiency in exploratory data analysis (EDA), feature engineering, and a solid understanding of data structures and statistical techniques.

Which databases should AI engineers be familiar with?

AI engineers should be familiar with both SQL and NoSQL databases. Krishak suggests MySQL, MongoDB, and Apache Cassandra as useful databases for AI projects.

What machine learning topics are covered in Krishak’s roadmap?

Krishak's roadmap covers a wide range of machine learning topics, including supervised and unsupervised learning algorithms like linear regression, decision trees, random forests, XGBoost, and clustering techniques.

How does Krishak approach deep learning in the roadmap?

Krishak covers deep learning by teaching about neural networks, including CNNs, RNNs, LSTMs, Transformers, and other advanced architectures, alongside practical implementations.

What is the role of MLOps in the AI engineer roadmap?

MLOps plays a critical role in the AI engineer roadmap as it focuses on the deployment, monitoring, and management of machine learning models in production. This includes tools for CI/CD, model versioning, and model monitoring using platforms like AWS, Azure, and Kubernetes.