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Large language models (LLMs) perform well on general tasks but struggle with specialized work that requires understanding proprietary data, internal processes, and industry-specific terminology. Supervised fine-tuning (SFT) adapts LLMs to these organizational contexts. SFT can be implemented through two distinct methodologies: Parameter-Efficient Fine-Tuning (PEFT), which updates only a subset of model parameters, offering faster training …

Building specialized AI without sacrificing intelligence: Nova Forge data mixing in action Read More »

Customer service teams face a persistent challenge. Existing chat-based assistants frustrate users with rigid responses, while direct large language model (LLM) implementations lack the structure needed for reliable business operations. When customers need help with order inquiries, cancellations, or status updates, traditional approaches either fail to understand natural language or can’t maintain context across multistep …

Build a serverless conversational AI agent using Claude with LangGraph and managed MLflow on Amazon SageMaker AI Read More »

Are you struggling to balance generative AI safety with accuracy, performance, and costs? Many organizations face this challenge when deploying generative AI applications to production. A guardrail that’s too strict blocks legitimate user requests, which frustrates customers. One that’s too lenient exposes your application to harmful content, prompt attacks, or unintended data exposure. Finding the …

Build safe generative AI applications like a Pro: Best Practices with Amazon Bedrock Guardrails Read More »

Google is partnering with the Massachusetts AI Hub to provide every Baystater with no-cost access to Google’s AI training. ​ 

There’s a lot of excitement right now about AI enabling mainframe application modernization. Boards are paying attention. CIOs are getting asked for a plan. AI is a genuine accelerator for COBOL modernization but to get results, AI needs additional context that source code alone can’t provide.Here’s what we’ve learned working with 400+ enterprise customers: mainframe …

Learnings from COBOL modernization in the real world Read More »

New alternatives, “understand” and “ask” buttons in Google Translate help you navigate the complexities of natural language. ​ 

Foundation models deliver impressive out-of-the-box performance for general tasks, but many organizations need models to consume their business knowledge. Model customization helps you bridge the gap between general-purpose AI and your specific business needs when building applications that require domain-specific expertise, enforcing communication styles, optimizing for specialized tasks like code generation, financial reasoning, or ensuring …

Reinforcement fine-tuning for Amazon Nova: Teaching AI through feedback Read More »

Modern large language model (LLM) deployments face an escalating cost and performance challenge driven by token count growth. Token count, which is directly related to word count, image size, and other input factors, determines both computational requirements and costs. Longer contexts translate to higher expenses per inference request. This challenge has intensified as frontier models …

Large model inference container – latest capabilities and performance enhancements Read More »

Our latest image generation model offers advanced world knowledge, production-ready specs, subject consistency and more, all at Flash speed. ​ 

Nano Banana 2 (Gemini 3.1 Flash Image) delivers Pro-level intelligence and fidelity for all image applications. ​