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Artificial Intelligence

Amazon Bedrock Runtime

2026-01-07T13:42:57+00:00

Bookmarks Amazon Bedrock Runtime Actions Key Data Types Inference Concepts Security Pricing Amazon Bedrock Runtime Cheat Sheet Amazon Bedrock Runtime is a high-performance, serverless API that enables developers to make inference requests to Foundation Models (FMs) available in Amazon Bedrock. It serves as the primary runtime interface for building generative AI applications, supporting use cases including text generation, multi-turn conversations, real-time streaming, image generation, embeddings, and more. The API is optimized for low latency and high throughput and provides unified access across multiple model providers.   Amazon Bedrock [...]

Amazon Bedrock Runtime2026-01-07T13:42:57+00:00

Amazon Bedrock API Reference

2026-01-07T13:33:19+00:00

Bookmarks Amazon Bedrock API Reference Common Parameters Amazon Bedrock API Reference Common Errors API Endpoint Structure Best Practices Amazon Bedrock API Reference Sheet Amazon Bedrock API Reference is the master specification for the Amazon Bedrock service. It encompasses protocols, authentication methods, endpoints, common parameters, and error-handling standards used across the entire Bedrock ecosystem (both the Control Plane and the Data Plane).   Amazon Bedrock API Reference Common Parameters Action: (String) Specifies the particular API action to be performed. Version: (String) Indicates the API version used for the request, formatted as [...]

Amazon Bedrock API Reference2026-01-07T13:33:19+00:00

A Beginner’s Guide to the Machine Learning Pipeline on GCP

2026-01-08T13:02:44+00:00

When people hear the term "machine learning," they often imagine complex math, advanced algorithms, or mysterious "AI magic" happening behind the scenes. In reality, machine learning on the cloud is far more practical and structured than it may sound. At its core, an ML pipeline is a series of steps that transform raw data into useful predictions. Think of it like a typical software workflow: You prepare your code You build the application You deploy it Users interact with it An ML pipeline follows the same idea, just with different building blocks. Instead of starting with source code, you begin [...]

A Beginner’s Guide to the Machine Learning Pipeline on GCP2026-01-08T13:02:44+00:00

Amazon Bedrock Prompt Management

2025-12-30T14:14:17+00:00

Bookmarks Core Concepts Supported Regions and Models Prerequisites Creating a Prompt Viewing Prompt Information Modifying a Prompt Testing a Prompt Optimizing a Prompt Deployment using Versions Deleting a Prompt Security Best Practices Pricing Amazon Bedrock Prompt Management Cheat Sheet Amazon Bedrock Prompt Management is a centralized prompt management system for generative AI, enabling easy creation, testing, versioning, and deployment of structured prompts while separating prompt engineering from application code. It offers key [...]

Amazon Bedrock Prompt Management2025-12-30T14:14:17+00:00

Amazon Titan

2025-12-30T14:40:34+00:00

Bookmarks Features Use Cases Amazon Titan Text Models Amazon Titan Text Embeddings Models Amazon Titan Multimodal Embeddings G1 Model Amazon Titan Image Generator G1 Models Pricing Amazon Titan Cheat Sheet Amazon Titan models are a family of powerful, general-purpose models pre-trained by AWS on massive datasets. They are designed to be used out of the box or fine-tuned with your own data, allowing you to adapt them for specific tasks without the need to annotate large volumes of training data. The Titan family consists [...]

Amazon Titan2025-12-30T14:40:34+00:00

The Year of the Agent: Anthropic’s Claude AI Models and Agents

2025-12-26T17:20:34+00:00

  Looking back on the past year of 2025 coding with the help of artificial intelligence, we can safely say that it was the year of agents, especially pioneered by Anthropic with Claude Code. AI products have matured to offer highly reliable agents that can understand, navigate, and work seamlessly on large codebases. It is the end of the old ways: of manually copying and pasting code into web AI applications. Agents are now actually part of the codebase, navigating around like a real developer. And they can now be left with long-running tasks on their own,opening and working on [...]

The Year of the Agent: Anthropic’s Claude AI Models and Agents2025-12-26T17:20:34+00:00

Automating PII Detection and Redaction with Amazon Comprehend

2026-01-08T08:21:54+00:00

Organizations today are entrusted with enormous amounts of sensitive information. Customer support logs, healthcare records, financial transactions, and even training datasets often contain Personally Identifiable Information (PII) such as names, phone numbers, email addresses, or credit card numbers. Protecting this information is not just a matter of compliance with regulations like GDPR, HIPAA, or PCI DSS. It is also central to maintaining customer trust and reducing the risk of data breaches. Amazon Comprehend, a managed natural language processing (NLP) service, provides a powerful way to automate the detection and redaction of PII. Instead of relying on manual review or custom [...]

Automating PII Detection and Redaction with Amazon Comprehend2026-01-08T08:21:54+00:00

How to Generate Simple Document Embeddings with Python

2025-12-10T05:58:07+00:00

Document embeddings are one of the simplest ways to give machines an understanding of text, and in our previous article, Document Embeddings Explained: A Guide for Beginners, we explored how they turn entire documents into dense numerical vectors that capture meaning and context. Now that you understand what embeddings are and why they’re useful for tasks like semantic search, classification, and clustering, this tutorial will show you how to generate them in practice using Python. Whether you’re working with short paragraphs, long articles, or a collection of documents, the steps in this guide will help you create embeddings that you [...]

How to Generate Simple Document Embeddings with Python2025-12-10T05:58:07+00:00

Open Cybersecurity Schema Framework (OCSF) and Amazon Security Lake

2025-12-06T12:04:18+00:00

Amazon Security Lake is a managed service that collects and stores security logs from AWS services, on-premises systems, and supported third-party tools. It automatically converts all incoming data into Apache Parquet and formats everything using the OCSF schema. This setup allows different kinds of security logs to follow one consistent structure instead of having separate formats. With this unified approach, teams no longer need to decode or reorganize data manually because Security Lake handles the normalization process for them. In this article, we will walk through what OCSF is, how Amazon Security Lake uses it, and why this combination makes [...]

Open Cybersecurity Schema Framework (OCSF) and Amazon Security Lake2025-12-06T12:04:18+00:00

Understanding the Agentic AI Security Framework: Made Easy

2026-02-04T13:22:03+00:00

Agentic AI is changing how we think about artificial intelligence. Instead of waiting for prompts, these systems can plan tasks, make decisions, and act on their own. They behave more like digital teammates than static tools, completing multi-step work and coordinating across apps, data, and even other agents all without constant human supervision. But with this new power comes new responsibility. When AI agents can access tools, call APIs, store memory, and influence other agents, the risks are no longer limited to “bad prompts” or one-time outputs. Autonomy introduces new attack surfaces: reasoning can be manipulated, memory can be poisoned, [...]

Understanding the Agentic AI Security Framework: Made Easy2026-02-04T13:22:03+00:00

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