2 posts tagged with "Deep Learning"

In the realm of deep learning, finetuning of pre-trained Large Language Models (LLMs) on private datasets is an excellent customization option to increase a model’s relevancy for a specific task. This practice is not only common, but also essential for developing specialized models, particularly for tasks like text classification and summarization.

In such scenarios, tools like MLflow are invaluable. Tracking tools like MLflow help to ensure that every aspect of the training process - metrics, parameters, and artifacts - are reproducibly tracked and logged, allowing for the analysis, comparison, and sharing of tuning iterations.

In the quickly evolving world of artificial intelligence, where generative AI has taken center stage, the landscape of machine learning is evolving at an unprecedented pace. There has been a surge in the use of cutting-edge deep learning (DL) libraries like Transformers, Tensorflow, and PyTorch to fine-tune these generative AI models for enhanced performance. As this trend accelerates, it's become clear that the tools used to build these models must rapidly evolve as well, particularly when it comes to managing and optimizing these deep learning workloads. MLflow offers a practical solution for managing the complexities of these machine learning projects.