Natural Language Processing (NLP) has significantly advanced with the rise of transformer models, starting with the groundbreaking "Attention Is All You Need" paper published in 2017, which introduced the transformer architecture. Transformer models such as GPT-3, BERT, and XLNet have become the cornerstone of NLP, expanding in size and capability, with the largest example being Microsoft’s Megatron-Turing NLG model with 530 billion parameters. These models, while effective, are costly to train, prompting smaller businesses and start-ups to rely on platforms like Hugging Face, which offers a community-driven ecosystem of pre-trained models and tools for NLP, vision, and audio tasks. Hugging Face provides resources such as the transformers library, tokenizers, and the accelerate library, which supports distributed training, and has gained popularity with over 61,000 stars on GitHub. The company recently expanded by acquiring Gradio, enhancing its ability to demo ML models, and emphasizes the importance of data preparation, a significant part of the machine learning workflow. To address data processing challenges, platforms like Qwak enable the automation of data cleaning and MLOps processes, facilitating the efficient deployment and scaling of Hugging Face models.