AWS Machine Learning Blog

NOVEMBER 7, 2024

In this second part, we expand the solution and show to further accelerate innovation by centralizing common Generative AI components. We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. They’re illustrated in the following figure.

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Generative AI AI AI Responsible AI 107

AWS Machine Learning Blog

JUNE 25, 2024

With Amazon Bedrock, developers can experiment, evaluate, and deploy generative AI applications without worrying about infrastructure management. Its enterprise-grade security, privacy controls, and responsible AI features enable secure and trustworthy generative AI innovation at scale.

Automation 119

Automation Prompt Engineer Prompt Engineering Categorization 119

AWS Machine Learning Blog

MAY 21, 2024

This blog post outlines various use cases where we’re using generative AI to address digital publishing challenges. However, when the article is complete, supporting information and metadata must be defined, such as an article summary, categories, tags, and related articles.

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Generative AI Prompt Engineer Prompt Engineering Metadata 123

The MLOps Blog

JUNE 27, 2023

The platform also offers features for hyperparameter optimization, automating model training workflows, model management, prompt engineering, and no-code ML app development. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support.

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Metadata Machine Learning Data Scientist Data Quality 134

AWS Machine Learning Blog

AUGUST 21, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Generative AI 123

Generative AI Prompt Engineer Prompt Engineering Automation 123

AWS Machine Learning Blog

JUNE 20, 2024

Additionally, evaluation can identify potential biases, hallucinations, inconsistencies, or factual errors that may arise from the integration of external sources or from sub-optimal prompt engineering. In this case, the model choice needs to be revisited or further prompt engineering needs to be done.

Auto-classification 126

Auto-classification LLM Prompt Engineer Prompt Engineering 126

Snorkel AI

MARCH 2, 2023

The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration. This goes into the model risk management in analyzing the metadata around the model, whether it’s fit for purpose with automated or human-in-the-loop capabilities.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration. This goes into the model risk management in analyzing the metadata around the model, whether it’s fit for purpose with automated or human-in-the-loop capabilities.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Furthermore, evaluating LLMs can also help mitigating security risks, particularly in the context of prompt data tampering. The following diagram illustrates this architecture.

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LLM Data Scientist Algorithm Generative AI 102