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About Ace Kenneth Batacandulo

Ace is AWS Certified, AWS Community Builder, and Cloud Consultant at Tutorials Dojo Pte. Ltd. He is also the Co-Lead Organizer of K8SUG Philippines and a member of the Content Committee for Google Developer Groups Cloud Manila. Ace actively contributes to the tech community through his volunteer work with AWS User Group PH, GDG Cloud Manila, K8SUG Philippines, and Devcon PH. He is deeply passionate about technology and is dedicated to exploring and advancing his expertise in the field.

What is Model Context Protocol (MCP)?

2025-11-16T12:35:08+00:00

An open, model‑agnostic protocol introduced by Anthropic in November 2024, designed to standardize how AI systems (huge language models, LLMs) connect with external data sources and tools via a JSON‑RPC interface. Often likened to a “USB‑C port for AI,” offering a universal interface rather than bespoke integrations per system. Key Benefits of MCP Provides a standardized interface so LLMs can easily connect to multiple tools and data sources without custom adapters. Solves the “N×M” problem, removing the need to build a unique connector for every AI–tool combination. Ensures structured and validated exchanges, supporting better debugging, version control, and reliability in [...]

What is Model Context Protocol (MCP)?2025-11-16T12:35:08+00:00

What is Federated Learning?

2025-08-26T16:51:20+00:00

A machine learning technique where multiple devices or servers collaboratively train a shared model without sharing raw data. Instead of sending data to a central server, only the model updates (gradients/parameters) are sent, keeping sensitive information local. Key Concepts Decentralized Training: Data stays on local devices (e.g., smartphones, IoT, edge devices). Model Aggregation: A central server collects and averages model updates to improve the global model. Privacy-Preserving: Minimizes risk of exposing personal or sensitive data. Communication Efficiency: Reduces the need for large-scale raw data transfer. Edge AI Integration: Often paired with edge computing for real-time AI. How Federated Learning Works [...]

What is Federated Learning?2025-08-26T16:51:20+00:00

What are Clustering Algorithms in Machine Learning?

2025-08-25T06:43:36+00:00

Clustering is an unsupervised learning technique that groups similar data points without predefined labels. It helps discover hidden patterns, segment data, and reduce dimensionality in datasets. Key Concepts Clustering: Grouping data points based on similarity or distance metrics. Unsupervised Learning: No labeled data; the model identifies structure independently. Distance Metrics: Commonly used metrics include Euclidean, Manhattan, and Cosine similarity. Popular Clustering Algorithms 1. K-Means Clustering Divides data into K clusters by minimizing the variance within each cluster. Fast, easy to implement, and works well with large datasets. It requires predefining K and is sensitive to outliers. Customer segmentation, image compression. [...]

What are Clustering Algorithms in Machine Learning?2025-08-25T06:43:36+00:00

Amazon DataZone

2025-08-15T18:27:14+00:00

Amazon DataZone Cheat Sheet Amazon DataZone is a fully managed data management service by AWS. Facilitates cataloging, discovery, sharing, and data governance across AWS, on-premises, and third-party sources. Enables organizations to implement a data mesh architecture, promoting decentralized data ownership and self-service analytics. Integrates seamlessly with AWS services like Amazon Redshift, Amazon Athena, AWS Glue, and AWS Lake Formation. Features Business Data Catalog: Organizes data assets within the business context, making them easily discoverable. Data Products: Groups related data assets into cohesive units for specific business use cases, simplifying access and management. Automated Metadata Generation: Utilizes large language models (LLMs) [...]

Amazon DataZone2025-08-15T18:27:14+00:00

Amazon Data Firehose

2025-08-15T18:25:17+00:00

Amazon Data Firehose Cheat Sheet Easily stream real-time data to AWS destinations without custom applications. Adjusts resources dynamically to match data volume. AWS Lambda supports data format conversion (e.g., Parquet, ORC) and custom transformations. Works seamlessly with Amazon S3, Redshift, OpenSearch, Splunk, and third-party HTTP endpoints. Features Real-Time Data Delivery: Streams data with minimal latency to multiple destinations. Data Buffering: Configurable buffer sizes and intervals to control data flow. Compression & Encryption: Supports GZIP and Snappy compression; integrates with AWS KMS for encryption. Monitoring & Alerts: Amazon CloudWatch metrics and alarms monitor delivery stream health. Dynamic Partitioning: Organizes data into [...]

Amazon Data Firehose2025-08-15T18:25:17+00:00

AWS Clean Rooms

2025-08-15T18:23:30+00:00

AWS Clean Rooms Cheat Sheet AWS Clean Rooms provide a secure, privacy-enhanced collaboration environment for analyzing shared datasets without exposing underlying data. Allows fast collaboration setup with minimal configuration, enabling users to analyze datasets where they reside (AWS or Snowflake). No need to move or extract data for collaborative analysis. Features Fast Setup: Use AWS Management Console or API to create collaboration spaces in minutes. Zero‑ETL Data Collaboration: Analyze data without transferring it from AWS or Snowflake. Privacy Controls: Differential Privacy: Protects against re-identification by obfuscating outputs. Cryptographic Computing: Keeps data encrypted during use and processing (C3R). Role-Based Access: Control [...]

AWS Clean Rooms2025-08-15T18:23:30+00:00

Amazon Cloud Directory

2025-12-28T16:54:01+00:00

Amazon Cloud Directory Cheat Sheet Amazon Cloud Directory is a fully managed, cloud-native directory service that handles complex hierarchical data structures, such as organizational charts, device registries, etc. Supports directories with hundreds of millions of objects and relationships, making it ideal for large organizations. You can design custom schemas for your specific needs and share schemas across multiple applications. No need for manual server management or scaling. AWS handles everything. Use hierarchical structures with flexible facets and attributes to organize data in a way that best fits your application. Service Status: Amazon Cloud Directory will no longer be open to [...]

Amazon Cloud Directory2025-12-28T16:54:01+00:00

AWS Entity Resolution

2025-08-14T10:35:00+00:00

AWS Entity Resolution Cheat Sheet AWS Entity Resolution is a fully managed service that helps organizations match, link, and enhance records across various customer, product, business, or healthcare data sources. It automates data integration by resolving duplicates and merging fragmented records. The service offers rule-based, machine learning-powered, and third-party matching techniques, ensuring high-quality, unified datasets. It also supports data encryption for security and compliance. Improving data quality enables better decision-making, enhanced customer profiles, and streamlined operations. Features Minimized Data Movement: Reads data where it resides (e.g., S3), reducing unnecessary data transfers. Flexible Data Input & Schema Mapping: Supports up to [...]

AWS Entity Resolution2025-08-14T10:35:00+00:00

Exploring the New “Summarize Results” Feature in Amazon CloudWatch Log Insights

2025-08-13T09:27:38+00:00

Logging is essential in cloud monitoring, but let's face it: Combining endless lines of log data can be a real-time disaster. Whether managing a complex infrastructure or troubleshooting an issue, finding the key insights buried in thousands of log entries can feel like searching for a needle in a haystack. That's where the new Summarize Results feature in Amazon CloudWatch Log Insights comes in. This game-changing update helps users instantly distill their log queries into key takeaways, saving valuable time and improving efficiency. Whether you're analyzing performance, security events, or application behavior, Summarize Results gives you a quick snapshot of [...]

Exploring the New “Summarize Results” Feature in Amazon CloudWatch Log Insights2025-08-13T09:27:38+00:00

Amazon DevOps Guru

2025-08-13T17:40:34+00:00

Amazon DevOps Guru Cheat Sheet Amazon DevOps Guru is a fully managed service that improves application performance and availability. Automates the detection and analysis of operational issues. Removes the burden of manual monitoring by identifying anomalies and generating actionable recommendations. Uses machine learning to analyze application metrics, logs, and events. Detects deviations from normal behavior to identify potential problems. Provides both reactive insights for current issues and proactive insights to help prevent future incidents. Notifies users when a potential problem is identified. Provides intelligent guidance to accelerate resolution. Features ML-Driven Insights: Detects anomalies across logs, metrics, events, and traces. Proactive + [...]

Amazon DevOps Guru2025-08-13T17:40:34+00:00

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