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Learn how to fine-tune large language models for enterprise use cases by utilizing approaches such as full fine-tuning, parameter efficient fine-tuning, and distillation.

Highlights and discussions from Apple's Workshop on Machine Learning for Health, including topics like translational research, fairness and robustness in data collection, safety and quality goals, privacy concerns, and applications in cardiology.

Zero-shot adaptive prompting method for large language models that bridges the performance gap between zero-shot and few-shot setups.

Advancing research at the intersection of AI and health through the selection of Takeda Fellows conducting pathbreaking research ranging from remote health monitoring for virtual clinical trials to ingestible devices for at-home, long-term diagnostics.

Deploy ML models from Amazon SageMaker Canvas to real-time endpoints in Amazon SageMaker, streamlining the process and making it accessible without code.

How to fine-tune large language models for enterprise use cases

Mexico-based IT Strategy achieved a 48% reduction in TCO by implementing ChromeOS devices, improving security and saving time and costs.

Zero-downtime scaling with enhanced efficiency and reliability using Memorystore for Redis Cluster

This blogpost discusses how Access Transparency and Access Approval provide access visibility and control to customers in Google Cloud for security and compliance requirements.

A decade of Kubernetes leadership: why Google Cloud should be your choice for Kubernetes

Discover the advancements and potential of generative AI in creating personalized recommendations and generating unstructured outputs.

Develop generative AI applications for personalized teaching and learning experiences using Amazon Bedrock and NLP models.