I am Amrit, with over 17 years of experience in the IT industry, beginning from the early days of Linux. My career started as a Linux System Administrator, later transitioning to an AIX Administrator (IBM P Series). With a background in Mathematics (Hons.), I have always had a strong interest in coding. Alongside system administration, I worked on automation using Python and shell scripting.

After joining my current company, I have been focused on DevOps pipelines, cloud infrastructure, and Python-based automation for the past six years. Over the last three years, I have also had the opportunity to work on data pipelines using Apache Spark, handling live streams of Tomcat data—a crucial part of data engineering.

Now, I am eager to advance into the field of AI/ML. My goal is to gain a deep understanding of how large language models (LLMs) work, including the mathematics behind them, as well as explore small language models (SLMs). I aim to learn how to customize and train these models with my own data to build applications such as automated chatbots or advisory tools for my customers.

I am seeking a structured roadmap to achieve this. While I have been learning various aspects of AI/ML, my approach has been haphazard. I now want to pursue a more organized learning plan within a fixed timeline of six months (or slightly more if needed).

Could you kindly provide me with a structured roadmap to help me achieve these goals?