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Advancing AI trust with new responsible AI tools, capabilities, and resources

AWS Machine Learning Blog

Automated Reasoning checks help prevent factual errors from hallucinations using sound mathematical, logic-based algorithmic verification and reasoning processes to verify the information generated by a model, so outputs align with provided facts and arent based on hallucinated or inconsistent data.

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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

offers a Prompt Lab, where users can interact with different prompts using prompt engineering on generative AI models for both zero-shot prompting and few-shot prompting. foundation models to help users discover, augment, and enrich data with natural language. To bridge the tuning gap, watsonx.ai

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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. Model management Teams typically manage their models, including versioning and metadata.

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Learnings From Building the ML Platform at Stitch Fix

The MLOps Blog

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. As you’ve been running the ML data platform team, how do you do that? Stefan: Yeah.

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