Fine-tuning
Recent items mentioning Fine-tuning across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
NewsWhen to choose CPU vs GPU: Databricks AI Runtime Explained
CPUs are best for data work like ETL, feature engineering, SQL, and classical machine learning, while GPUs are designed for deep learning workloads such as fine-tuning LLMs and training neural networks. Databricks AI Runtime simplifies GPU usage by providing serverless Nvidia GPUs, removing the need for manual infrastructure setup and allowing seamless transitions between CPU for data prep and GPU for model training within the Databricks environment.
TutorialsHow Large Language Models (LLMs) Work - Full Explanation | Chapter 04
Large Language Models (LLMs) are text-based neural networks trained on massive data to predict the next word (token), operating through tokenization, vector embeddings, and a transformer architecture. LLMs undergo pre-training, supervised fine-tuning, and reinforcement learning from human feedback to become helpful, safe, and aligned, with concepts like context length, knowledge cut-off, and hallucination defining their capabilities and limitations.
AI Applications: Tools, Use Cases, and Platforms
AI applications span four capability tiers, each with distinct data requirements and evaluation frameworks, and enterprise deployments often stall due to inadequate data infrastructure. Production-grade model development, from prompt engineering to pretraining, is increasingly accessible with open-source LLMs, but requires pre-built governance and monitoring infrastructure for successful deployment at scale.
EventsPatrick Wendell, Co-founder and VP of Engineering on Building Production-Quality AI Systems
NewsSponsored by: Labelbox | Unlocking Enterprise AI with Your Proprietary Data and Foundation Models
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