Agree. I got into my 1st ML role last year. All self-taught. I’ve done all sorts of work - from ETL, CV, sentiment pipeline (mostly SWE stuff) and now LLM-based Information retrieval systems. My work mostly revolves around applied ML but I do have an interest in knowing the bits and bytes of ML as well. So currently teaching myself all about transformers and lms.
But it is also true that, earlier getting started with ML was easy - no need for heavy machinery/resources. But nowadays, you will need high computing power to even get started on learning something about large language models.
Agree. I got into my 1st ML role last year. All self-taught. I’ve done all sorts of work - from ETL, CV, sentiment pipeline (mostly SWE stuff) and now LLM-based Information retrieval systems. My work mostly revolves around applied ML but I do have an interest in knowing the bits and bytes of ML as well. So currently teaching myself all about transformers and lms.
But it is also true that, earlier getting started with ML was easy - no need for heavy machinery/resources. But nowadays, you will need high computing power to even get started on learning something about large language models.