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joshuasanti

Home » Collections for Joshua Emmanuel Santiago

About Joshua Emmanuel Santiago

Joshua, a college student at Mapúa University pursuing BS IT course, serves as an intern at Tutorials Dojo.

GitHub CLI

2026-01-28T10:28:58+00:00

GitHub CLI Cheat Sheet GitHub CLI (gh) is a command-line interface to GitHub for use in your terminal. It helps you interact with many GitHub features—issues, pull requests, checks, releases, and more—without needing to switch to a web browser. Key Concepts Authentication: Required for most operations. gh uses OAuth tokens or Personal Access Tokens (PATs) to authenticate with GitHub. Repository: The primary location for your project's code and collaboration. Many gh commands operate within the context of a repository. Issue: A unit for tracking work, such as bug reports or feature requests. Pull Request (PR): A proposal to merge changes from one branch into [...]

GitHub CLI2026-01-28T10:28:58+00:00

Github Pages

2026-01-22T11:41:49+00:00

Github Pages Cheat Sheet GitHub Pages provides straightforward static website hosting directly from your GitHub repositories. This service transforms repository files into complete websites, handling the deployment and hosting without requiring separate infrastructure. It works seamlessly with static site generators and supports custom domains, making it suitable for project documentation, personal portfolios, organizational sites, and blogs. Key Concepts Concepts Description Repository Types User/Organization sites (one per account, username.github.io repo) and Project sites (unlimited, any repo with Pages enabled) Source Branches Options: root of main branch, /docs folder on main, or dedicated gh-pages branch Static Site Generator Jekyll comes pre-configured, but you can use any generator (Hugo, Gatsby) with GitHub Actions Build Process [...]

Github Pages2026-01-22T11:41:49+00:00

GitHub Packages

2026-01-23T04:58:14+00:00

GitHub Packages Cheat Sheet GitHub Packages is an integrated package hosting service that allows you to host software packages—including containers, npm modules, and Java libraries—privately or publicly alongside your source code. It leverages your existing GitHub permissions, billing, and workflows to provide a seamless experience for managing your software dependencies and distribution. Key Concepts Package: A bundled unit of software (code, dependencies, metadata) Registry: A storage and distribution system for packages Scope: Organization/user namespace for packages Versioning: Semantic versioning support for package management Visibility: Public (open source) or Private (requires authentication) Supported Package Registries GitHub Packages supports multiple package ecosystems. [...]

GitHub Packages2026-01-23T04:58:14+00:00

Github Actions

2026-01-23T05:48:37+00:00

GitHub Actions Cheat Sheet GitHub Actions is a continuous integration and continuous delivery (CI/CD) platform that allows you to automate your build, test, and deployment pipeline. You can create workflows that build and test every pull request to your repository, or deploy merged pull requests to production. Key Components Workflow: An automated procedure that you add to your repository. Defined by a YAML file in .github/workflows/. Event: A specific activity that triggers a workflow run (e.g., push, pull_request, release). Job: A set of steps that execute on the same runner. Jobs run in parallel by default. Step: An individual task that can run commands [...]

Github Actions2026-01-23T05:48:37+00:00

Amazon Sagemaker Model Registry Cheat Sheet

2026-01-23T03:30:10+00:00

Bookmarks Core Concepts Features Implementation Integration Best Practices Pricing    A dedicated, fully-managed metadata store and governance hub within Amazon SageMaker designed to catalog, version, track, audit, and deploy machine learning (ML) models throughout their entire lifecycle. It serves as the single source of truth for model inventory, lineage, and approval states, enabling collaboration between data scientists, ML engineers, and governance teams while enforcing consistency and compliance in model deployment workflows. Amazon SageMaker Model Registry Core Concepts Model Package Group A logical container that organizes all iterations of a single model solving [...]

Amazon Sagemaker Model Registry Cheat Sheet2026-01-23T03:30:10+00:00

Amazon SageMaker Model Monitor Cheat Sheet

2026-01-12T09:02:21+00:00

Bookmarks Features How It Works Implementation Use Cases Integration Best Practices Pricing    A fully-managed, automated service within Amazon SageMaker that continuously monitors the quality of machine learning (ML) models in production. It automatically detects data drift and model performance decay, sending alerts so you can maintain model accuracy over time without building custom monitoring tools. Features Automated Data Capture & Collection Configures your SageMaker endpoints to capture a specified percentage of incoming inference requests and model predictions. This data, enriched with metadata (timestamp, endpoint name), is automatically stored in your [...]

Amazon SageMaker Model Monitor Cheat Sheet2026-01-12T09:02:21+00:00

Amazon Sagemaker Jumpstart Cheat Sheet

2026-01-12T07:23:56+00:00

Bookmarks Features How It Works Implementation Use Cases Integration Best Practices Pricing    A centralized machine learning hub within Amazon SageMaker AI designed to drastically reduce the time and expertise required to build, train, and deploy models. It provides instant access to a curated catalog of production-ready assets.   Features Foundation Models Hub Access a broad selection of state-of-the-art foundation models from providers like AI21 Labs, Cohere, Meta, Mistral AI, and Stability AI, alongside hundreds of open-source models from Hugging Face. You can evaluate, compare, and perform tasks like text summarization, [...]

Amazon Sagemaker Jumpstart Cheat Sheet2026-01-12T07:23:56+00:00

Amazon Sagemaker Ground Truth Cheat Sheet

2026-01-07T05:39:07+00:00

Bookmarks Features How It Works Implementation Use Cases Integration Best Practices Pricing    A fully managed data labeling service that uses a combination of human workers and machine learning to build high-quality datasets for training machine learning models. It provides built-in workflows, multiple workforce options, and automated labeling to reduce cost and time.   Features Automated Data Labeling (Active Learning) Uses a machine learning model to pre-label datasets and continuously learns from human feedback. It sends only low-confidence data to human reviewers, reducing labeling costs by up to 70% compared to [...]

Amazon Sagemaker Ground Truth Cheat Sheet2026-01-07T05:39:07+00:00

Amazon Bedrock Data Automation Cheat Sheet

2025-12-30T06:07:02+00:00

Bookmarks Features Use Cases Implementation Security Best Practices Pricing    Amazon Bedrock Data Automation is a purpose-built service for transforming complex, unstructured content—such as invoices, contracts, forms, and research papers—into structured data. It handles the entire pipeline, from document parsing and classification to advanced information extraction using natural language and computer vision, enabling you to build scalable document workflows integrated directly with Knowledge Bases, databases, and analytics tools.   Amazon Bedrock Data Automation Features Multimodal Document Understanding Processes a wide range of document types and formats, including scanned PDFs, digital PDFs, JPEG/PNG images, [...]

Amazon Bedrock Data Automation Cheat Sheet2025-12-30T06:07:02+00:00

Amazon Bedrock Flows Cheat Sheet

2025-12-30T05:34:25+00:00

Bookmarks Features Use Cases Implementation Security Best Practices Pricing    Amazon Bedrock Flows is a core feature for implementing production-ready, complex generative AI applications. It abstracts the heavy lifting of coding integrations, state management, and deployment pipelines into a drag-and-drop visual interface or API. This allows teams—from developers to subject-matter experts—to collaborate and rapidly iterate on AI workflows, moving from prototyping to scalable, versioned deployments in minutes.   Amazon Bedrock Flows Features Visual, Low-Code/No-Code Builder Design workflows using a drag-and-drop interface in Amazon Bedrock Studio. Link nodes representing Prompts, Foundation Models (FMs), Knowledge [...]

Amazon Bedrock Flows Cheat Sheet2025-12-30T05:34:25+00:00

AWS, Azure, and GCP Certifications are consistently among the top-paying IT certifications in the world, considering that most companies have now shifted to the cloud. Upskill and earn over $150,000 per year with an AWS, Azure, or GCP certification!

Follow us on LinkedIn, Facebook, or join our Slack study group. More importantly, answer as many practice exams as you can to help increase your chances of passing your certification exams on your first try!