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At a basic level, Machine Learning (ML) technology learns from data to make predictions. Businesses use their data with an ML-powered personalization service to elevate their customer experience. This approach allows businesses to use data to derive actionable insights and help grow their revenue and brand loyalty. Amazon Personalize accelerates your digital transformation with ML, […]

We are witnessing a rapid increase in the adoption of large language models (LLM) that power generative AI applications across industries. LLMs are capable of a variety of tasks, such as generating creative content, answering inquiries via chatbots, generating code, and more. Organizations looking to use LLMs to power their applications are increasingly wary about data privacy to ensure trust and safety is maintained within their generative AI applications. This includes handling customers’ personally identifiable information (PII) data properly. It also includes preventing abusive and unsafe content from being propagated to LLMs and checking that data generated by LLMs follows the same principles. In this post, we discuss new features powered by Amazon Comprehend that enable seamless integration to ensure data privacy, content safety, and prompt safety in new and existing generative AI applications.

In the recent past, using machine learning (ML) to make predictions, especially for data in the form of text and images, required extensive ML knowledge for creating and tuning of deep learning models. Today, ML has become more accessible to any user who wants to use ML models to generate business value. With Amazon SageMaker […]

Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. Hyperparameters are the knobs and levers that we use to adjust the training process, such as learning rate, batch size, regularization strength, and others, depending on the specific model and task at hand. Exploring hyperparameters involves systematically varying […]

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance. In order to fulfill regulatory and compliance requirements, the […]

Generative AI models for coding companions are mostly trained on publicly available source code and natural language text. While the large size of the training corpus enables the models to generate code for commonly used functionality, these models are unaware of code in private repositories and the associated coding styles that are enforced when developing […]

In this post, we show you how to create a MAP connector to AWS HealthImaging, which is reusable in applications built with the MONAI Deploy App SDK, to integrate with and accelerate image data retrieval from a cloud-native DICOM store to medical imaging AI workloads. The MONAI Deploy SDK can be used to support hospital operations. We also demonstrate two hosting options to deploy MAP AI applications on SageMaker at scale.

Large language models (LLMs) with their broad knowledge, can generate human-like text on almost any topic. However, their training on massive datasets also limits their usefulness for specialized tasks. Without continued learning, these models remain oblivious to new data and trends that emerge after their initial training. Furthermore, the cost to train new LLMs can […]

Recently, teachers and institutions have looked for different ways to incorporate artificial intelligence (AI) into their curriculums, whether it be teaching about machine learning (ML) or incorporating it into creating lesson plans, grading, or other educational applications. Generative AI models, in particular large language models (LLMs), have dramatically sped up AI’s impact on education. Generative […]

Today, personally identifiable information (PII) is everywhere. PII is in emails, slack messages, videos, PDFs, and so on. It refers to any data or information that can be used to identify a specific individual. PII is sensitive in nature and includes various types of personal data, such as name, contact information, identification numbers, financial information, […]