Amazon S3
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KubeMastery
KodeKloud
Amazon S3 (Amazon Simple Storage Service) is the storage platform by Amazon Web Services (AWS) that provides an object storage with high availability, low latency and high durability. S3 can store any type of object and can serve as storage for internet applications, backups, disaster recovery, data archives, big data sets and multimedia.
I wanted to learn Kubernetes, but the options were either "build a local lab" or pay for expensive classes built around cloud environments. I wanted something you could just open and learn from. So I built a browser-based cluster simulation with lessons to follow along.
It behaves like Kubernetes, but it's just JavaScript under the hood. Enough to run kubectl commands, create resources, and watch their lifecycle play out. There's also a live cluster visualizer so you can see your nodes, Pods, and containers in real time.
Once you've covered the concepts, you can jump into CKA drills. Each drill is a standalone, time-boxed scenario that mirrors the real exam format: a broken cluster state, a concrete task, and a pass-or-fail outcome.
The goal isn't to simulate the exam. It's to build the reflex of diagnosing and fixing things quickly, so that when you sit the real exam, the commands and the reasoning are already automatic.
Amazon S3
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TLS at the API boundary encrypts the payload in transit, but your application is responsible for what happens to the document after the response arrives. If you're writing the rendered PDF to disk, a message queue, or cloud storage, that persistence layer needs its own encryption at rest. An unencrypted file sitting in an Amazon S3 bucket with overly permissive ACLs falls outside what the API provider's TLS covers. - Source: dev.to / 2 months ago
SAM CLI generates the SAMCodeUriServices mapping so that each collection value resolves to its own build artifact. At package time, those paths become Amazon S3 URIs. I don't need to manage any of this. - Source: dev.to / 3 months ago
Fine-tuning adapts an FM to a specific use case with proprietary training data. Titan, Cohere, and Meta models support fine-tuning via Amazon Bedrock. Text models need labelled prompt-completion pairs; image models need Amazon Simple Storage Service (Amazon S3) paths linked to descriptions. Secure training data with Amazon Virtual Private Cloud (Amazon VPC) + AWS PrivateLink. - Source: dev.to / 3 months ago
You need to understand vector stores for semantic and hybrid search using Amazon OpenSearch Service and Amazon Simple Storage Service (Amazon S3). Prompt caching helps reduce costs by reusing previously processed prompts. Amazon Bedrock Prompt Management simplifies the creation, evaluation, versioning, and sharing of prompts to help you get the best responses from foundation models. Flow orchestration with Amazon... - Source: dev.to / 4 months ago
All fine-tuning used Amazon SageMaker Training Jobs โ no instance provisioning, no SSH, no manual teardown. You provide a training script and an S3 dataset path, specify the instance type, and SageMaker handles the rest. - Source: dev.to / 5 months ago
AWS Lambda - Automatic, event-driven compute service
KodeKloud - We are a fast growing EdTech startup on trending technologies in IT and Cloud Computing.
Google Cloud Storage - Google Cloud Storage offers developers and IT organizations durable and highly available object storage.
Amazon CloudFront - Amazon CloudFront is a content delivery web service.
Amazon EC2 - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.
Amazon AWS - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.