Be an ML Engineer, not an API Caller

In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) like OpenAI's ChatGPT and Google's Gemini have taken center stage. These powerful models, accessible through simple API calls, have opened up new possibilities for startups and developers, enabling them to integrate advanced natural language processing capabilities into their applications with ease. However, amidst the excitement and hype surrounding these LLMs, a concerning trend has emerged: aspiring Machine Learning Engineers (MLEs) are jumping on the bandwagon without acquiring a solid foundation in the core concepts of machine learning. In this blog post, we will delve into the reasons why relying solely on LLM APIs is a misguided approach for beginners, explore how companies are capitalizing on the hype, and emphasize the crucial importance of strong fundamental ML knowledge for senior roles.

The rise of LLMs has undeniably revolutionized the field of natural language processing. These models, trained on vast amounts of textual data using state-of-the-art deep learning architectures, have demonstrated remarkable capabilities in generating human-like responses, answering questions, and assisting with a wide range of tasks, from content generation to code completion. The ease of accessing these capabilities through simple API calls has attracted many developers, especially those with backgrounds in web development or other programming domains. The promise of quickly building intelligent applications without the need to delve into the intricacies of machine learning has proven to be a tempting proposition for many.

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