This guest post was written by Mulay Ahmed and Caroline Lima-Lane of Principal Financial Group. The content and opinions in this post are those of

Choosing the right large language model (LLM) for your use case is becoming both increasingly challenging and essential. Many teams rely on one-time (ad hoc)

Generative AI is rapidly reshaping the music industry, empowering creators—regardless of skill—to create studio-quality tracks with foundation models (FMs) that personalize compositions in real time.

Organizations often face challenges when implementing single-shot fine-tuning approaches for their generative AI models. The single-shot fine-tuning method involves selecting training data, configuring hyperparameters, and

Artificial Intelligence (AI) is transforming the quick-service restaurant industry, particularly in drive-thru operations where efficiency and customer satisfaction intersect. Traditional systems create significant obstacles in

Multimodal fine-tuning represents a powerful approach for customizing vision large language models (LLMs) to excel at specific tasks that involve both visual and textual information.

Since the launch of Amazon Nova at AWS re:Invent 2024, we have seen adoption trends across industries, with notable gains in operational efficiency, compliance, and

Building AI agents that remember user interactions requires more than just storing raw conversations. While Amazon Bedrock AgentCore short-term memory captures immediate context, the real

Training state-of-the-art large language models (LLMs) demands massive, distributed compute infrastructure. Meta’s Llama 3, for instance, ran on 16,000 NVIDIA H100 GPUs for over 30.84

Scala stands out as a versatile programming language that combines object-oriented and functional programming approaches. By running on the Java Virtual Machine (JVM), it maintains