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Amp Up Your Game with AWS Amplify: Deploy and Authenticate Your First App

2026-02-01T11:54:43+00:00

As an active student volunteer in various organizations, specifically as a Logistics Member, I know firsthand how overwhelming the work can get. When tasks come pouring in, booking venues, buying supplies, and tracking receipts, it is easy to lose track of the details. To solve this, I decided to build a solution. That's how Fin-N-Log (short for Finance and Logistics) was born—a personal dashboard to track logistics expenses and to-do lists. My goal was simple: reduce stress and eliminate manual checking for student volunteers like me. In this guide, I'll show you exactly how I built and deployed this tracker [...]

Amp Up Your Game with AWS Amplify: Deploy and Authenticate Your First App2026-02-01T11:54:43+00:00

Amazon Nova: Engineering the Future of Agentic AI

2026-02-03T13:45:47+00:00

The generative AI (GenAI) revolution has fundamentally changed how organizations extract value from data. Large language models (LLMs) excel at understanding and generating human-like text, but their true enterprise value emerges only when they can access proprietary data and take real-world action. While vector databases and retrieval-augmented generation (RAG) gave LLMs memory, Amazon Nova provides execution and specialization. In this article, we break down the Amazon Nova model family, with a deep focus on Nova Act and Nova Forge, and explain how they enable a shift from passive chatbots to autonomous, enterprise-grade AI agents. What Is the Amazon Nova Model [...]

Amazon Nova: Engineering the Future of Agentic AI2026-02-03T13:45:47+00:00

Why AWS Feels Overwhelming at First (And How to Approach It Properly)

2026-01-29T06:40:48+00:00

Getting started with AWS can feel overwhelming, especially when you’re exposed to dozens of services, dashboards, and acronyms right away. Many beginners assume that struggling means they’re “not cut out” for cloud computing, but that’s rarely true. The real challenge is not intelligence or effort, it’s understanding how to approach learning AWS fundamentals without getting lost in the noise. Once you shift how you think about AWS, the platform becomes far more approachable and logical. AWS was not designed to be learned all at once, even though it often feels that way at the beginning. The platform grew over time to [...]

Why AWS Feels Overwhelming at First (And How to Approach It Properly)2026-01-29T06:40:48+00:00

Bring Your Own Container Made Easy: Introducing AWS ml-container-creator

2026-01-27T18:51:07+00:00

If you’ve ever struggled to package your ML model in a custom Docker image for SageMaker, the new ml-container-creator tool is here to help. This friendly open-source wizard guides you through building a SageMaker-compatible container without all the usual Docker headaches. It’s like having an assistant that writes your Dockerfile, server code, and config files for you, so you can focus on your model. What is BYOC on SageMaker? BYOC stands for Bring Your Own Container. In SageMaker, BYOC means you supply your own Docker image with everything needed to serve your ML model (the code, libraries, dependencies, etc.). AWS [...]

Bring Your Own Container Made Easy: Introducing AWS ml-container-creator2026-01-27T18:51:07+00:00

What to Do After Passing a Cloud Certification: A 60-day Guide

2026-01-26T04:02:59+00:00

What to do after passing a cloud certification is a common question for many learners who expect the exam to feel like a turning point. Weeks or months of study finally lead to a passing score, the exam closes, and the pressure lifts. For a brief moment, it feels like progress has been made in a very real way. Then reality sets in, nothing immediately changes. There are no sudden job offers, no clear roadmap for what comes next, and no obvious signal that the certification has moved your career forward. This moment is common, yet rarely discussed. Many people [...]

What to Do After Passing a Cloud Certification: A 60-day Guide2026-01-26T04:02:59+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 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

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

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!