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amazon sagemaker

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Mastering Cloud-Based Semantic Search: Advanced Cloud Search Architectures Made Easy

2025-09-26T10:14:10+00:00

In today's AI-driven world, finding information based on meaning and context rather than exact keywords has become crucial. Good thing we now have vector search, enabling semantic search, personalized recommendations, image similarity, and more. However, building and managing vector search infrastructure can be complex. Fortunately, AWS offers zero-infrastructure managed services that let you implement powerful vector search capabilities without worrying about servers, scaling, or maintenance. Let's walk through creating a simple yet effective vector search demo using AWS services within the Free Tier so you can follow along without incurring costs. What is Vector Search? Vector search is a groundbreaking [...]

Mastering Cloud-Based Semantic Search: Advanced Cloud Search Architectures Made Easy2025-09-26T10:14:10+00:00

Amazon SageMaker Clarify

2025-08-29T07:18:48+00:00

Bookmarks Key Capabilities Core Components Configuration Components Bias Metrics Overview SHAP Explainability Validate Your Knowledge Amazon SageMaker Clarify Cheat Sheet Amazon SageMaker Clarify is a SageMaker AI feature for detecting bias and explaining model predictions. Supports both pre-training and post-training bias analysis. Provides feature attribution to explain how input features influence predictions. Can monitor deployed models for bias drift and feature attribution drift over time. Key Capabilities Bias Detection Pre-training bias: Analyzes datasets before model training. Post-training bias: Evaluates model predictions for fairness across facets. Supports binary, multiclass, and regression tasks. Interpreting [...]

Amazon SageMaker Clarify2025-08-29T07:18:48+00:00

Retrieval-Augmented Generation (RAG) for Foundation Model Customization

2024-12-02T06:01:45+00:00

Artificial Intelligence (AI) has rapidly advanced, pushing the limits of what machines can accomplish. However, one significant challenge remains: ensuring that AI responses are both accurate and contextually relevant while being up-to-date. This is where Retrieval-Augmented Generation (RAG) comes in—a cutting-edge approach that integrates the capabilities of data retrieval with advanced AI generation techniques. In this blog, we will explore the details of RAG, discussing its benefits, applications, and how to implement it using AWS. Understanding Retrieval-Augmented Generation (RAG) RAG (Retrieval-Augmented Generation) incorporates real-time data retrieval into the generative process. Unlike traditional models that depend solely on pre-trained data, RAG [...]

Retrieval-Augmented Generation (RAG) for Foundation Model Customization2024-12-02T06:01:45+00:00

AWS Glue DataBrew

2024-04-12T14:30:21+00:00

Bookmarks Features Components Pricing References AWS Glue DataBrew Cheat Sheet AWS Glue DataBrew is a tool designed to streamline your data analysis process. It allows you to interact with your data directly, eliminating the need for complex coding. With its extensive library of over 250 pre-built transformations, you can easily clean, normalize, and format your data, preparing it for insightful analysis. AWS Glue DataBrew is commonly used for: Reducing the time required to prepare data for analytics and machine learning. Automating data preparation tasks with a wide range of ready-made transformations. Facilitating collaboration [...]

AWS Glue DataBrew2024-04-12T14:30:21+00:00

Distributed Data Parallel Training with TensorFlow and Amazon SageMaker Distributed Training Library

2024-01-22T00:58:08+00:00

Introduction In the realm of machine learning, the ability to train models effectively and efficiently stands as a cornerstone of success. As datasets grow exponentially and models become more complex, traditional single-node training methods increasingly fall short. This is where distributed training enters the picture, offering a scalable solution to this growing challenge. Distributed Training Overview Distributed training is a technique used to train machine learning models on large datasets more efficiently. By splitting the workload across multiple compute nodes, it significantly reduces training time. There are two main strategies in distributed training: data parallelism, where the dataset is partitioned [...]

Distributed Data Parallel Training with TensorFlow and Amazon SageMaker Distributed Training Library2024-01-22T00:58:08+00:00

Securing Machine Learning Pipelines: Best Practices in Amazon SageMaker

2024-01-17T00:45:41+00:00

Introduction In today's digital era, the importance of security in machine learning (ML) pipelines cannot be overstated. As ML systems increasingly become integral to business operations and decision-making, ensuring the integrity and security of these systems is paramount. A breach or a flaw in an ML pipeline can lead to compromised data, erroneous decision-making, and potentially catastrophic consequences for businesses and individuals alike. This section will delve into why securing ML pipelines is crucial, highlighting the potential risks and impacts of security lapses. Short Introduction to Amazon SageMaker Amazon SageMaker is a fully managed service that provides every developer and [...]

Securing Machine Learning Pipelines: Best Practices in Amazon SageMaker2024-01-17T00:45:41+00:00

Securing LLMs with Guardrails for Amazon Bedrock

2024-01-03T00:32:13+00:00

One of the pillars of the AWS Well-Architected Framework is security. It is a foundational concept when running your workloads in the cloud to think about privacy, access limits, compliance with regulatory requirements, and data protection; and this includes Amazon Bedrock. Along with several AI announcements during the keynote of AWS CEO, Adam Selipsky during AWS re:Invent 2023 was Guardrails for Amazon Bedrock. As AI technology evolves and becomes more mature, it makes sense to also reinvent the way usage is handled by security safeguards. Guardrails for Amazon Bedrock allow security policies to be applied across foundational models, to fulfill [...]

Securing LLMs with Guardrails for Amazon Bedrock2024-01-03T00:32:13+00:00

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