CloudCurls | AWS Cloud, Serverless, DevOps and Automation Tutorials

Introduction Cloud computing is one of the fastest-growing industries in the world. Companies are moving their applications, databases, AI workloads, and infrastructure to the cloud because it provides scalability, security, reliability, and cost savings. From startups to large enterprises, every organization today needs cloud professionals. This creates huge opportunities for freshers who want to build a strong career in technology. If you are confused about: Which cloud platform to start with Whether to learn AWS, Azure, or Google Cloud Which certifications to take Important Tools Cloud Engineers Should Learn What projects to build Common mistakes should avoid Then this guide is for you. This blog is designed specifically for beginners and freshers who want a detailed roadmap to become a cloud engineer in 2026. What is Cloud Computing? Cloud computing means using servers, storage, databases, networking, software, and computing resources over the internet instead of managing everythin...

AWS has introduced a major update to Amazon S3 — you can now mount your S3 bucket like a file system. This new capability, called S3 Files , changes how developers and data teams interact with storage on AWS. For years, S3 was known only as object storage. Now, it behaves much closer to a traditional file system, making it easier to work with existing tools and applications. What is Amazon S3 Files? S3 Files allows you to mount your S3 bucket as if it were a local disk on services like EC2, ECS, EKS, or Lambda. Once mounted, you can use standard file operations such as listing directories, reading files, or writing new data without any special APIs. This means developers no longer need to think in terms of objects and keys. Instead, they can simply work with files and folders, just like they would on a normal Linux system. Why This Matters Earlier, AWS storage worked like this: Amazon S3 → Cheap, scalable object storage Amazon EFS → File system for applications 👉 You had to ...

Kiro is AWS's agentic AI IDE that goes beyond code generation — it plans, designs, executes, and documents your features as a true development partner. Built on Code OSS (the open-source foundation of VS Code), Kiro introduces spec-driven development to transform how software gets built from idea to production. This guide breaks down what Kiro is, what makes it different, and when it delivers the most value for developers. What Is Kiro? Kiro is an agentic AI IDE from AWS that understands the intent behind your prompts and orchestrates entire development workflows — planning, designing, coding, and documenting — without you having to manage any of that manually. What Kiro Handles For You Requirements documentation – Auto-generated in EARS notation from your natural language prompt System design – Analyzes your codebase and proposes architecture that fits your existing stack Task sequencing – An ordered, dependency-aware implementation plan created before a single line...

AWS Lambda is one of the most widely adopted serverless computing services on Amazon Web Services. It allows developers to run code without provisioning or managing servers, revolutionizing how modern applications are built and deployed. While AWS Lambda is powerful and cost-effective for many use cases, it's not suitable for every workload. This comprehensive guide will help you understand when to use AWS Lambda and when to choose alternative AWS compute services. What Is AWS Lambda? AWS Lambda is a serverless, event-driven compute service that automatically runs your code in response to events and scales instantly based on demand. What You Don't Manage Servers – No infrastructure to provision or maintain Operating systems – Automatic patching and updates Capacity planning – Automatic scaling from zero to thousands of concurrent executions Lambda Pricing Model: Pay Only for What You Use Number of invocations – First 1 million requests per month are free Execution dur...

While RNNs are great at handling sequences, they sometimes struggle with long-term dependencies . Imagine trying to remember a detail from 20 steps ago—RNNs often “forget” that information. That’s where  LSTM (Long Short-Term Memory) and later BiLSTM (Bidirectional LSTM) models are being used. These models are game-changers for solar power forecasting. Long Short-Term Memory LSTM is an advanced type of RNN designed to remember information for longer periods. It has special structures called gates: Forget Gate: Decides what information to throw away. Input Gate: Decides what new information to store. Output Gate: Decides what the model should output at each step. LSTMs handle long-term dependencies much better than regular RNNs. Since the same dataset was used here as well, you can read more about it in my earlier RNN blog . Model Results for LSTM The training graph shows that the loss decreases steadily as the number of epochs increases. This means the model learns patterns in th...

Introduction When we think about renewable energy, solar power often comes to mind first. But there’s one big challenge: solar energy is not constant . It changes with weather, seasons, and even time of the day. To make solar power more reliable, predicting its future output becomes very important. That’s where machine learning - especially Recurrent Neural Networks (RNNs)  comes in. What is RNN A Recurrent Neural Network (RNN) is a type of deep learning model that is great at handling sequential data —data that comes in order, like time-series or speech. Unlike regular neural networks, RNNs remember past information by looping their outputs back into the network. This makes them perfect for tasks like predicting solar power output, stock prices, or even natural language processing. How RNN Works RNNs take input data step by step.  They store the output of the previous step and use it along with the new input to make better predictions.  This memory-like ability helps th...

Introduction If you’ve done any data work in Python, chances are you’ve used Pandas —it’s been the go-to library for data analysis and data preparation for years. But as datasets keep getting bigger and performance demands rise, a new player has entered the scene: Polars . Think of it as Pandas’ faster, more modern library. Both are great at handling data, but they differ quite a bit when it comes to speed, scalability, and the way they’re designed In this blog, we’ll dive into the differences between Pandas and Polars, and help you decide which one fits your use case. Pandas vs Polars Both Pandas and Polars can play an important role in data preparation and data analysis. Pandas: Pandas can integrated easily with s cikit-learn , Matplotlib, TensorFlow, and PyTorch. Built on top of  NumPy and designed for in-memory datasets Pandas is ideal for small to medium dataset. Polars: Uses  Apache Arrow memory model for efficient storage Designed to be multi-threaded and more memory...