Today’s machine learning landscape is defined by large language and foundation models. Unlike traditional ML models of the past, where models were built and trained for specific tasks from scratch, large language models (LLMs) and foundation models now serve as the starting point for most applications. While pre-trained on massive datasets, foundation models often require post-training to reach peak effectiveness for specific use cases.
While few organizations may undertake the pre-training of foundation models, mastering post-training techniques is essential for every team. This involves:
- Gaining access to a diverse set of foundation models,
- Building flexible, scalable infrastructure to support large-scale experiments and computations,
- Crafting a robust data strategy that can accommodate a wide range of post-training methods.
Mastering post-training techniques is no longer optional—it’s essential. Teams must ensure they have access to the right tools, infrastructure, and models to stay competitive in delivering optimized, high-performance solutions.
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