Roadmap introduction

0:00 this AI engineering road map takes you from core fundamentals to Advanced AI implementations it covers essential mathematics machine learning deep learning and large language models providing you with the exact skills needed to thrive as an AI engineer in 2025 whether you're starting fresh or upgrading your skills this road map offers a clear path to success with hands-on experience and Industry relevant insights T from lunar Tech developed this course imagine being at the Forefront of one of the most transformative fields of our time where technology meets Innovation and changes the world welcome to the AI engineering road map of 2025 my name is D Vasan from lunar Tech and I'm absolutely exciting to be here with you today to dive into this highly requested topic together we will will explore everything that you need to know to navigate this exciting world of artificial intelligence and AI engineering to set yourself up for success in this field in this video we

1:11 are going to break down the step-by-step road map for becoming a worldclass AI engineer here is what we are going to cover first we will Define what AI engineering is and how it feds into this broader ecosystem of AI and data science next we will explore the real world applications of AI engineering showcasing its really strong power transformative impact across different Industries then we will dive into the must have versus nice to have skills helping you to understand exactly where to focus your efforts and your time finally we will go to step-by-step process so the skill sets that you need to master outlining the essential topics to help you become a job ready AI engineer this session is packed with unique insights and practical tips that you won't find any URS so stay tuned without further Ado let's get started so let's start with the basics what is AI engineering AI engineering is this practice of Designing building and deploying AI systems that solve real world problems it sits in this intersection of software engineering machine learning and data science and here is how it fits into this broader Tech world and the ecosystem so the data scientists often focus on analyzing data

2:53 or predicting something or developing models AI Engineers take these models and make them work in the real world settings and with much more advanced models they create systems that process data make decisions and deliver actionable insights for example in the healthcare a data scientist might develop a machine learning model to detect the tumors in x-rays an AI engineer brings this to the next level he ensures that the model is integrated into Hospital Systems runs in real time and works reliably under different conditions also AI Engineers they work with much more advanced models like deep learning models or neural network based models so data science principles system design optimization machine learning deep learning is what all combines into one place which is AI engineering it's not just about building models it's about making sure that those models actually solve problems and deliver value for the business or this public Enterprise and that's why AI engineering is such a critical role in today's Tech ecosystem it's where this Cutting Edge