#aws elastic service

[codeonedigest]

Create Elastic Container Service in AWS | Run Nodejs JavaScript API in E... Full Video Link - https://youtu.be/SfkHutfNTHYHi, a new video to create aws ecs elastic container service aws ecs setup & run nodejs javascript api in ECS cluster service published on codeonedigest youtube channel. @awscloud @YouTube @codeonedigest  #awsecs #nodejs #dockerimage

[jcmarchi]

Deploying Large Language Models on Kubernetes: A Comprehensive Guide

New Post has been published on https://thedigitalinsider.com/deploying-large-language-models-on-kubernetes-a-comprehensive-guide/

Deploying Large Language Models on Kubernetes: A Comprehensive Guide

Large Language Models (LLMs) are capable of understanding and generating human-like text, making them invaluable for a wide range of applications, such as chatbots, content generation, and language translation.

However, deploying LLMs can be a challenging task due to their immense size and computational requirements. Kubernetes, an open-source container orchestration system, provides a powerful solution for deploying and managing LLMs at scale. In this technical blog, we’ll explore the process of deploying LLMs on Kubernetes, covering various aspects such as containerization, resource allocation, and scalability.

Understanding Large Language Models

Before diving into the deployment process, let’s briefly understand what Large Language Models are and why they are gaining so much attention.

Large Language Models (LLMs) are a type of neural network model trained on vast amounts of text data. These models learn to understand and generate human-like language by analyzing patterns and relationships within the training data. Some popular examples of LLMs include GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and XLNet.

LLMs have achieved remarkable performance in various NLP tasks, such as text generation, language translation, and question answering. However, their massive size and computational requirements pose significant challenges for deployment and inference.

Why Kubernetes for LLM Deployment?

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. It provides several benefits for deploying LLMs, including:

Scalability: Kubernetes allows you to scale your LLM deployment horizontally by adding or removing compute resources as needed, ensuring optimal resource utilization and performance.

Resource Management: Kubernetes enables efficient resource allocation and isolation, ensuring that your LLM deployment has access to the required compute, memory, and GPU resources.

High Availability: Kubernetes provides built-in mechanisms for self-healing, automatic rollouts, and rollbacks, ensuring that your LLM deployment remains highly available and resilient to failures.

Portability: Containerized LLM deployments can be easily moved between different environments, such as on-premises data centers or cloud platforms, without the need for extensive reconfiguration.

Ecosystem and Community Support: Kubernetes has a large and active community, providing a wealth of tools, libraries, and resources for deploying and managing complex applications like LLMs.

Preparing for LLM Deployment on Kubernetes:

Before deploying an LLM on Kubernetes, there are several prerequisites to consider:

Kubernetes Cluster: You’ll need a Kubernetes cluster set up and running, either on-premises or on a cloud platform like Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), or Azure Kubernetes Service (AKS).

GPU Support: LLMs are computationally intensive and often require GPU acceleration for efficient inference. Ensure that your Kubernetes cluster has access to GPU resources, either through physical GPUs or cloud-based GPU instances.

Container Registry: You’ll need a container registry to store your LLM Docker images. Popular options include Docker Hub, Amazon Elastic Container Registry (ECR), Google Container Registry (GCR), or Azure Container Registry (ACR).

LLM Model Files: Obtain the pre-trained LLM model files (weights, configuration, and tokenizer) from the respective source or train your own model.

Containerization: Containerize your LLM application using Docker or a similar container runtime. This involves creating a Dockerfile that packages your LLM code, dependencies, and model files into a Docker image.

Deploying an LLM on Kubernetes

Once you have the prerequisites in place, you can proceed with deploying your LLM on Kubernetes. The deployment process typically involves the following steps:

Building the Docker Image

Build the Docker image for your LLM application using the provided Dockerfile and push it to your container registry.

Creating Kubernetes Resources

Define the Kubernetes resources required for your LLM deployment, such as Deployments, Services, ConfigMaps, and Secrets. These resources are typically defined using YAML or JSON manifests.

Configuring Resource Requirements

Specify the resource requirements for your LLM deployment, including CPU, memory, and GPU resources. This ensures that your deployment has access to the necessary compute resources for efficient inference.

Deploying to Kubernetes

Use the kubectl command-line tool or a Kubernetes management tool (e.g., Kubernetes Dashboard, Rancher, or Lens) to apply the Kubernetes manifests and deploy your LLM application.

Monitoring and Scaling

Monitor the performance and resource utilization of your LLM deployment using Kubernetes monitoring tools like Prometheus and Grafana. Adjust the resource allocation or scale your deployment as needed to meet the demand.

Example Deployment

Let’s consider an example of deploying the GPT-3 language model on Kubernetes using a pre-built Docker image from Hugging Face. We’ll assume that you have a Kubernetes cluster set up and configured with GPU support.

Pull the Docker Image:

bashCopydocker pull huggingface/text-generation-inference:1.1.0

Create a Kubernetes Deployment:

Create a file named gpt3-deployment.yaml with the following content:

apiVersion: apps/v1 kind: Deployment metadata: name: gpt3-deployment spec: replicas: 1 selector: matchLabels: app: gpt3 template: metadata: labels: app: gpt3 spec: containers: - name: gpt3 image: huggingface/text-generation-inference:1.1.0 resources: limits: nvidia.com/gpu: 1 env: - name: MODEL_ID value: gpt2 - name: NUM_SHARD value: "1" - name: PORT value: "8080" - name: QUANTIZE value: bitsandbytes-nf4

This deployment specifies that we want to run one replica of the gpt3 container using the huggingface/text-generation-inference:1.1.0 Docker image. The deployment also sets the environment variables required for the container to load the GPT-3 model and configure the inference server.

Create a Kubernetes Service:

Create a file named gpt3-service.yaml with the following content:

apiVersion: v1 kind: Service metadata: name: gpt3-service spec: selector: app: gpt3 ports: - port: 80 targetPort: 8080 type: LoadBalancer

This service exposes the gpt3 deployment on port 80 and creates a LoadBalancer type service to make the inference server accessible from outside the Kubernetes cluster.

Deploy to Kubernetes:

Apply the Kubernetes manifests using the kubectl command:

kubectl apply -f gpt3-deployment.yaml kubectl apply -f gpt3-service.yaml

Monitor the Deployment:

Monitor the deployment progress using the following commands:

kubectl get pods kubectl logs <pod_name>

Once the pod is running and the logs indicate that the model is loaded and ready, you can obtain the external IP address of the LoadBalancer service:

kubectl get service gpt3-service

Test the Deployment:

You can now send requests to the inference server using the external IP address and port obtained from the previous step. For example, using curl:

curl -X POST http://<external_ip>:80/generate -H 'Content-Type: application/json' -d '"inputs": "The quick brown fox", "parameters": "max_new_tokens": 50'

This command sends a text generation request to the GPT-3 inference server, asking it to continue the prompt “The quick brown fox” for up to 50 additional tokens.

Advanced topics you should be aware of

While the example above demonstrates a basic deployment of an LLM on Kubernetes, there are several advanced topics and considerations to explore:

_*]:min-w-0″ readability=”131.72387362124″>

1. Autoscaling

Kubernetes supports horizontal and vertical autoscaling, which can be beneficial for LLM deployments due to their variable computational demands. Horizontal autoscaling allows you to automatically scale the number of replicas (pods) based on metrics like CPU or memory utilization. Vertical autoscaling, on the other hand, allows you to dynamically adjust the resource requests and limits for your containers.

To enable autoscaling, you can use the Kubernetes Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA). These components monitor your deployment and automatically scale resources based on predefined rules and thresholds.

2. GPU Scheduling and Sharing

In scenarios where multiple LLM deployments or other GPU-intensive workloads are running on the same Kubernetes cluster, efficient GPU scheduling and sharing become crucial. Kubernetes provides several mechanisms to ensure fair and efficient GPU utilization, such as GPU device plugins, node selectors, and resource limits.

You can also leverage advanced GPU scheduling techniques like NVIDIA Multi-Instance GPU (MIG) or AMD Memory Pool Remapping (MPR) to virtualize GPUs and share them among multiple workloads.

3. Model Parallelism and Sharding

Some LLMs, particularly those with billions or trillions of parameters, may not fit entirely into the memory of a single GPU or even a single node. In such cases, you can employ model parallelism and sharding techniques to distribute the model across multiple GPUs or nodes.

Model parallelism involves splitting the model architecture into different components (e.g., encoder, decoder) and distributing them across multiple devices. Sharding, on the other hand, involves partitioning the model parameters and distributing them across multiple devices or nodes.

Kubernetes provides mechanisms like StatefulSets and Custom Resource Definitions (CRDs) to manage and orchestrate distributed LLM deployments with model parallelism and sharding.

4. Fine-tuning and Continuous Learning

In many cases, pre-trained LLMs may need to be fine-tuned or continuously trained on domain-specific data to improve their performance for specific tasks or domains. Kubernetes can facilitate this process by providing a scalable and resilient platform for running fine-tuning or continuous learning workloads.

You can leverage Kubernetes batch processing frameworks like Apache Spark or Kubeflow to run distributed fine-tuning or training jobs on your LLM models. Additionally, you can integrate your fine-tuned or continuously trained models with your inference deployments using Kubernetes mechanisms like rolling updates or blue/green deployments.

5. Monitoring and Observability

Monitoring and observability are crucial aspects of any production deployment, including LLM deployments on Kubernetes. Kubernetes provides built-in monitoring solutions like Prometheus and integrations with popular observability platforms like Grafana, Elasticsearch, and Jaeger.

You can monitor various metrics related to your LLM deployments, such as CPU and memory utilization, GPU usage, inference latency, and throughput. Additionally, you can collect and analyze application-level logs and traces to gain insights into the behavior and performance of your LLM models.

6. Security and Compliance

Depending on your use case and the sensitivity of the data involved, you may need to consider security and compliance aspects when deploying LLMs on Kubernetes. Kubernetes provides several features and integrations to enhance security, such as network policies, role-based access control (RBAC), secrets management, and integration with external security solutions like HashiCorp Vault or AWS Secrets Manager.

Additionally, if you’re deploying LLMs in regulated industries or handling sensitive data, you may need to ensure compliance with relevant standards and regulations, such as GDPR, HIPAA, or PCI-DSS.

7. Multi-Cloud and Hybrid Deployments

While this blog post focuses on deploying LLMs on a single Kubernetes cluster, you may need to consider multi-cloud or hybrid deployments in some scenarios. Kubernetes provides a consistent platform for deploying and managing applications across different cloud providers and on-premises data centers.

You can leverage Kubernetes federation or multi-cluster management tools like KubeFed or GKE Hub to manage and orchestrate LLM deployments across multiple Kubernetes clusters spanning different cloud providers or hybrid environments.

These advanced topics highlight the flexibility and scalability of Kubernetes for deploying and managing LLMs.

Conclusion

Deploying Large Language Models (LLMs) on Kubernetes offers numerous benefits, including scalability, resource management, high availability, and portability. By following the steps outlined in this technical blog, you can containerize your LLM application, define the necessary Kubernetes resources, and deploy it to a Kubernetes cluster.

However, deploying LLMs on Kubernetes is just the first step. As your application grows and your requirements evolve, you may need to explore advanced topics such as autoscaling, GPU scheduling, model parallelism, fine-tuning, monitoring, security, and multi-cloud deployments.

Kubernetes provides a robust and extensible platform for deploying and managing LLMs, enabling you to build reliable, scalable, and secure applications.

[techdirectarchive]

How to create an EC2 Instance

Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable computing capacity in the cloud. Amazon EC2 offers many options that help you build and run virtually any application. With these possibilities, getting started with EC2 is quick and easy to do. In this article, we shall discuss how to create an EC2 Instance. Please see the Various ways to restart an AWS…

[atharvasys]

AWS ECS : Scalable and Cost effective Architecture

ECS is an AWS service to executes Docker as containers. An orchestration that makes it possible to start isolated container instances is called Docker. Docker containers may be set up and monitored on an ECS cluster using ECS, which runs on top of Docker.

[infosectrain03]

Amazon's Elastic File System (EFS) is a versatile and scalable storage solution that caters to general-purpose workloads. It can attach to multiple Amazon Web Services (AWS) compute instances and on-premises servers, providing a unified resource for data storage and applications in various environments.

[mariacallous]

On a 5K screen in Kirkland, Washington, four terminals blur with activity as artificial intelligence generates thousands of lines of code. Steve Yegge, a veteran software engineer who previously worked at Google and AWS, sits back to watch.

“This one is running some tests, that one is coming up with a plan. I am now coding on four different projects at once, although really I’m just burning tokens,” Yegge says, referring to the cost of generating chunks of text with a large language model (LLM).

Learning to code has long been seen as the ticket to a lucrative, secure career in tech. Now, the release of advanced coding models from firms like OpenAI, Anthropic, and Google threatens to upend that notion entirely. X and Bluesky are brimming with talk of companies downsizing their developer teams—or even eliminating them altogether.

When ChatGPT debuted in late 2022, AI models were capable of autocompleting small portions of code—a helpful, if modest step forward that served to speed up software development. As models advanced and gained “agentic” skills that allow them to use software programs, manipulate files, and access online services, engineers and non-engineers alike started using the tools to build entire apps and websites. Andrej Karpathy, a prominent AI researcher, coined the term “vibe coding” in February, to describe the process of developing software by prompting an AI model with text.

The rapid progress has led to speculation—and even panic—among developers, who fear that most development work could soon be automated away, in what would amount to a job apocalypse for engineers.

“We are not far from a world—I think we’ll be there in three to six months���where AI is writing 90 percent of the code,” Dario Amodei, CEO of Anthropic, said at a Council on Foreign Relations event in March. “And then in 12 months, we may be in a world where AI is writing essentially all of the code,” he added.

But many experts warn that even the best models have a way to go before they can reliably automate a lot of coding work. While future advancements might unleash AI that can code just as well as a human, until then relying too much on AI could result in a glut of buggy and hackable code, as well as a shortage of developers with the knowledge and skills needed to write good software.

David Autor, an economist at MIT who studies how AI affects employment, says it’s possible that software development work will be automated—similar to how transcription and translation jobs are quickly being replaced by AI. He notes, however, that advanced software engineering is much more complex and will be harder to automate than routine coding.

Autor adds that the picture may be complicated by the “elasticity” of demand for software engineering—the extent to which the market might accommodate additional engineering jobs.

“If demand for software were like demand for colonoscopies, no improvement in speed or reduction in costs would create a mad rush for the proctologist's office,” Autor says. “But if demand for software is like demand for taxi services, then we may see an Uber effect on coding: more people writing more code at lower prices, and lower wages.”

Yegge’s experience shows that perspectives are evolving. A prolific blogger as well as coder, Yegge was previously doubtful that AI would help produce much code. Today, he has been vibe-pilled, writing a book called Vibe Coding with another experienced developer, Gene Kim, that lays out the potential and the pitfalls of the approach. Yegge became convinced that AI would revolutionize software development last December, and he has led a push to develop AI coding tools at his company, Sourcegraph.

“This is how all programming will be conducted by the end of this year,” Yegge predicts. “And if you're not doing it, you're just walking in a race.”

The Vibe-Coding Divide

Today, coding message boards are full of examples of mobile apps, commercial websites, and even multiplayer games all apparently vibe-coded into being. Experienced coders, like Yegge, can give AI tools instructions and then watch AI bring complex ideas to life.

Several AI-coding startups, including Cursor and Windsurf have ridden a wave of interest in the approach. (OpenAI is widely rumored to be in talks to acquire Windsurf).

At the same time, the obvious limitations of generative AI, including the way models confabulate and become confused, has led many seasoned programmers to see AI-assisted coding—and especially gung-ho, no-hands vibe coding—as a potentially dangerous new fad.

Martin Casado, a computer scientist and general partner at Andreessen Horowitz who sits on the board of Cursor, says the idea that AI will replace human coders is overstated. “AI is great at doing dazzling things, but not good at doing specific things,” he said.

Still, Casado has been stunned by the pace of recent progress. “I had no idea it would get this good this quick,” he says. “This is the most dramatic shift in the art of computer science since assembly was supplanted by higher-level languages.”

Ken Thompson, vice president of engineering at Anaconda, a company that provides open source code for software development, says AI adoption tends to follow a generational divide, with younger developers diving in and older ones showing more caution. For all the hype, he says many developers still do not trust AI tools because their output is unpredictable, and will vary from one day to the next, even when given the same prompt. “The nondeterministic nature of AI is too risky, too dangerous,” he explains.

Both Casado and Thompson see the vibe-coding shift as less about replacement than abstraction, mimicking the way that new languages like Python build on top of lower-level languages like C, making it easier and faster to write code. New languages have typically broadened the appeal of programming and increased the number of practitioners. AI could similarly increase the number of people capable of producing working code.

Bad Vibes

Paradoxically, the vibe-coding boom suggests that a solid grasp of coding remains as important as ever. Those dabbling in the field often report running into problems, including introducing unforeseen security issues, creating features that only simulate real functionality, accidentally running up high bills using AI tools, and ending up with broken code and no idea how to fix it.

“AI [tools] will do everything for you—including fuck up,” Yegge says. “You need to watch them carefully, like toddlers.”

The fact that AI can produce results that range from remarkably impressive to shockingly problematic may explain why developers seem so divided about the technology. WIRED surveyed programmers in March to ask how they felt about AI coding, and found that the proportion who were enthusiastic about AI tools (36 percent) was mirrored by the portion who felt skeptical (38 percent).

“Undoubtedly AI will change the way code is produced,” says Daniel Jackson, a computer scientist at MIT who is currently exploring how to integrate AI into large-scale software development. “But it wouldn't surprise me if we were in for disappointment—that the hype will pass.”

Jackson cautions that AI models are fundamentally different from the compilers that turn code written in a high-level language into a lower-level language that is more efficient for machines to use, because they don’t always follow instructions. Sometimes an AI model may take an instruction and execute better than the developer—other times it might do the task much worse.

Jackson adds that vibe coding falls down when anyone is building serious software. “There are almost no applications in which ‘mostly works’ is good enough,” he says. “As soon as you care about a piece of software, you care that it works right.”

Many software projects are complex, and changes to one section of code can cause problems elsewhere in the system. Experienced programmers are good at understanding the bigger picture, Jackson says, but “large language models can't reason their way around those kinds of dependencies.”

Jackson believes that software development might evolve with more modular codebases and fewer dependencies to accommodate AI blind spots. He expects that AI may replace some developers but will also force many more to rethink their approach and focus more on project design.

Too much reliance on AI may be “a bit of an impending disaster,” Jackson adds, because “not only will we have masses of broken code, full of security vulnerabilities, but we'll have a new generation of programmers incapable of dealing with those vulnerabilities.”

Learn to Code

Even firms that have already integrated coding tools into their software development process say the technology remains far too unreliable for wider use.

Christine Yen, CEO at Honeycomb, a company that provides technology for monitoring the performance of large software systems, says that projects that are simple or formulaic, like building component libraries, are more amenable to using AI. Even so, she says the developers at her company who use AI in their work have only increased their productivity by about 50 percent.

Yen adds that for anything requiring good judgement, where performance is important, or where the resulting code touches sensitive systems or data, “AI just frankly isn't good enough yet to be additive.”

“The hard part about building software systems isn't just writing a lot of code,” she says. “Engineers are still going to be necessary, at least today, for owning that curation, judgment, guidance and direction.”

Others suggest that a shift in the workforce is coming. “We are not seeing less demand for developers,” says Liad Elidan, CEO of Milestone, a company that helps firms measure the impact of generative AI projects. “We are seeing less demand for average or low-performing developers.”

“If I'm building a product, I could have needed 50 engineers and now maybe I only need 20 or 30,” says Naveen Rao, VP of AI at Databricks, a company that helps large businesses build their own AI systems. “That is absolutely real.”

Rao says, however, that learning to code should remain a valuable skill for some time. “It’s like saying ‘Don't teach your kid to learn math,’” he says. Understanding how to get the most out of computers is likely to remain extremely valuable, he adds.

Yegge and Kim, the veteran coders, believe that most developers can adapt to the coming wave. In their book on vibe coding, the pair recommend new strategies for software development including modular code bases, constant testing, and plenty of experimentation. Yegge says that using AI to write software is evolving into its own—slightly risky—art form. “It’s about how to do this without destroying your hard disk and draining your bank account,” he says.

[cinderswrites]

F A I T H :: 30 Fics in 30 Days 3256 / 30000 words. 11% done!

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This challenge is something I made based off the 30k November challenge. I plan on writing one short story per day every day of November, and since I know I'll probably blow past the 30k mark, I changed the name.

-⛧°。 ⋆༺♱༻⋆。 °⛧-⛧°。 ⋆༺♱༻⋆。 °⛧-⛧°。 ⋆༺♱༻⋆。 °⛧-

"Faith" is a story about challenging beliefs, rebellion, and maybe hope a little bit.

-⛧°。 ⋆༺♱༻⋆。 °⛧-⛧°。 ⋆༺♱༻⋆。 °⛧-⛧°。 ⋆༺♱༻⋆。 °⛧-

:: WC: 3,256 :: CW: mentions of religion, mentions of suicide ::

!! The opinions of the characters in this story do not reflect my personal beliefs or stances on these particular subjects. !!

-⛧°。 ⋆༺♱༻⋆。 °⛧-⛧°。 ⋆༺♱༻⋆。 °⛧-⛧°。 ⋆༺♱༻⋆。 °⛧-

The sun was hot against her back as Audrina trailed behind her father and his wife up the sidewalk to his church. It stood stark white against the deep emeralds of the pine trees behind it. Trees that were evergreen and thick with needles. A forest that called to her, whispered her name almost.

She wanted to be anywhere other than here, that was for damn sure.

Don’t say that.

The harsh voice of her father scolded her in her mind, even though he appeared to be calm and in a pleasant mood as he unlocked the heavy oak door to the church. The twins, her step-mother’s children from a previous marriage, stood off to the side, whispering between each other and shooting her glances with their identical pale blue eyes.

Eyes that reminded her of a dead fish.

“Audrina,” her father said, his voice stern and nasally. Her cornflower blue eyes, a blue that was actually pleasing to look at, jerked towards him like a deer in headlights. “Make sure the pews are clean and everyone’s hymnals are in their place.”

He was a shorter man, rather portly in the middle even if he refused to acknowledge it, if the strain on his white button shirt was any indication. His round face was clean-shaven and there was a tiny cut on the side of his jaw he hadn’t noticed. The blood had dried on their drive to the church and it was what her eyes focused on as he issued his demands.

Always so demanding.

She’d been free of it for the last ten years, at least. But now she was home, back in this tiny awful town somewhere in Nowhere, Colorado. They could hardly be considered a part of what the lower states were often called: the Bible Belt.

Since she was a good daughter, though, she nodded and walked through the door he held open for her. Inside the church, it was dusty and humid, typically in this time of year. It was still early summer. She’d only returned from college a few weeks ago and already she hated this place. The dust was almost thick enough to choke her, and she almost wondered why none of the parishioners helped clean the place up.

She took her time making sure everything was neat and organized like she had been taught as a young girl. They always arrived hours before service anyway. In the background, she could hear the twins still whispering, watching her like the creepy little things they were. Mary Louanne’s daughters might just be the devil’s spawn, she thought to herself.

Some time had passed and about a half-hour into the service, Audrina slipped out the back of the chapel, making her way to the front door. She was suffocating in this heat, made worse by the many bodies occupying the pews and the thick wafting perfumes that hung heavily in the air from women fanning themselves. It was a church, not a gala. She never understood why the women dressed up to talk about Jesus.

Audrina pushed open the heavy oak door, closing it behind her and turning around, wiping the sweat off her brow. Her thick wavy chestnut brown hair hung almost sadly around her shoulders, also weighed down by the humidity. If only she had an elastic, or a ribbon–

She paused in her motions, taking on the deer in the headlights look again. There was a man some odd ten feet or so from the oak door. He had his hands shoved inside the pockets of his leather jacket–Who the hell wears leather in the eighty degree summer heat?

His dark eyes flicked to her face, startled to see her as well. He shifted his weight from hip to hip for a moment, looking away, his expression troubled. She lowered her hand and folded it in front of her with the other one, watching him carefully. She’d seen him around before, usually after services, hovering on the edge. Watching the parishioners leave. Watching her father thank people and talk with them about the day’s sermon as he always loved to drone on about it long after it was over.

The man reached up to run a hand through his sandy blond hair, a messy thing that fell in loose waves around his face and down his neck. He had ear piercings and tattoos, and there was stubble on his face like he hadn’t bothered to clean up. “So, uh-“ his voice was a smooth and deep timbre, like the low hum of a bass guitar in the rock songs she sneakily listened to sometimes. “Sermon not… good today?” His question was awkward and it was clear he didn’t know what to say.

Audrina looked down with a small smile, trying not to be impolite by laughing. She glanced up at him, shaking her head slightly. “No, no,” she stuck her hands in the pockets of her dress skirt, pockets she’d sewn in herself. “It’s rather… muggy in there. I needed some fresh air, although… it’s not much better out here, is it?”

She raised her hand, blocking the hot sun and looking up in the clear blue sky. For a summer day, it was wonderful. For a Sunday, it was torture.

The man chuckled and scuffed his black boot against the ground, “Yeah, I suppose it isn’t.”

Feeling a little less on edge, she took a few steps towards him until she stood beside him, turning to face the church and look at it as he had been. “Do you need… help, or anything?” she asked with a side glance.

He seemed to stiffen a bit when she came near, even though she’d kept a polite distance between them. “Ah, no…” he hesitated. “Guess I’m just… curious, maybe.”

“About… the church?” she pressed.

“Sorta,” he rubbed the back of his neck, clearly struggling with what he was trying to say. “I don’t know.”

His sigh made her turn her head to look at him. “Well… do you want to go in for service?” she tried a different approach.

His brown eyes met hers as he scoffed, wrinkling his nose. Up close, she could see a smattering of freckles on his face. “No,” he said quickly.

Audrina shrugged, “Alright, then.”

As she looked back at the church, her hands resting in her pockets, she could feel his gaze on the side of her face. Like he was studying her. He spoke up after a minute or so of silence, “What’s your name? I see you around with the priest and his wife sometimes.”

“Audrina,” her blue eyes flicked to him as she answered, before moving back to the church. “Audrina Montgomery. The priest is my father, and his wife is my step-mother, Mary Louanne. Her daughters, the twin girls that are usually hovering around, are Sarah and Jane.”

“Mm,” he grunted in response, looking away. “Those twins, your step-sisters… they’re kinda freaky looking, yeah?” he pointed out.

That brought out a surprise laugh from her, and she tilted her head down, unable to hide her grin. “Yeah, yeah they are,” she agreed. She turned her face towards him again. “They’re always together and always whispering and watching me. It’s definitely… freaky.”

He chuckled in response, and she found it to be a comforting noise for an odd reason, “That’s fucking weird.”

Her lips twitched a little at his cursing. She wasn’t entirely a prude, she’d heard people cursing in college and occasionally did it herself, but she wasn’t used to it. Not in Lone River, at least. “So, who are you?” she questioned, keeping her voice casual and friendly as she looked at him once more.

The man shifted his weight around again, as if debating on telling her. “Miah Banks,” he finally said.

“Miah? That’s a name I haven’t heard,” she raised an eyebrow.

“It’s short for Jeremiah,” he shrugged. “Reagan gave the name to me a while ago.”

“Reagan?” her eyes widened.

As in Reagan, Reagan? The scary ruthless leader of that biker gang on the edge of town her father warned her about when she returned home? Did that mean he was also a gang member?

Miah frowned slightly, his guard going up immediately. “… Yeah, Reagan,” his tone was flat.

Audrina’s face softened, “I’m sorry, I didn’t mean to sound like that. Truth be told, I haven’t been in Lone River for a lot of years, so I’m still unfamiliar with all… well, you know.”

He nodded, relaxing a little, “I see. I guess I’m just used to people writing us off as devils in leather, or somethin’.”

She let out a soft snort. “Now, Devils in Leather would kind of be a cool name,” she smiled playfully.

Miah’s stoic-looking face cracked into a lopsided grin as he chuckled, “It does sound good. Maybe I should bring it up to Reagan and see what he thinks.”

“Speaking of leather, how are you not melting?” she looked pointedly at his black leather jacket. He didn’t even seem to be sweating.

He shrugged one of his broad shoulders, “Used to it.”

“Well, I’m not, so I’m going to move over that way for some shade,” she started walking towards the tree line. She could already feel the cooler wind from the forest beckoning her, wrapping around her ankles and pulling her forward like invisible tendrils begging her to get closer.

Miah followed behind her without hesitation, his boots crunching against the dry grass and pebble mixture. Audrina sat beneath one of the pine trees, carefully folding her skirt underneath her and behind mindful of the pine needles. He sat down near her, raising one of his knees and draping an arm on top of it. “So, you’re the priest’s daughter, then?” he asked, wanting clarification.

“Unfortunately,” she rolled her eyes, sighing. The cooler air beneath the shade of the trees was already helping her feel less like she was boiling.

He glanced at her curiously, “And you sound happy about that, I see.”

“It’s not that-“ she sighed. “It’s… complicated.”

“Well, life’s kinda like that, I noticed,” he offered casually. “Something you want to get off your mind?”

Audrina studied his face for a moment, wondering if he was being genuine. He wasn’t anything like the monsters her father tried to paint the Sons of Night as. If anything, he seemed rather… calm. She looked away, back to the church once again. “He sent me away,” she explained. “To a private school. Catholic. I lived there until I graduated, and then I went to college, and now I’m home, and it’s…”

“Complicated?”

She glanced at him and smiled. “Yeah. Complicated,” she ran a hand through her thick locks and sighed.

“… It sounds like it might have been rough on you,” Miah stated. “That school, I mean.”

“It was,” she nodded. “I wasn’t able to mail letters to any of my friends back home, and they all moved on and grew up without me. Everything had to be done according to their rules or we were punished unfairly. My father remarried and didn’t even invite me to the wedding. I didn’t even know I had little sisters until I came back, so that should tell you how much he kept in touch with me.”

She looked down at her hands, scratching at some dry skin on her cuticle. Just the thought of that awful school almost made her shiver out of habit. Miah’s voice was gentle as he spoke to her, “You must have been pretty lonely, then. Still, even.”

Audrina looked up at him, not bothering to hide the vulnerable expression on her face. “Yeah… I am,” she huffed a bitter laugh.

Miah’s eyes were filled with something like understanding as he met hers. “Sorry you went through that.”

She shook her head, waving her hand dismissively. “It’s… fine. I’m fine, really. Just figuring out where to go from here,” she let out another sigh.

“My old man wasn’t much of a father, either,” he sympathized. “He tried, but his mind was too… messed up, or somethin’.”

Miah had pulled a cigarette out of the pack he pulled from his jacket, sticking it between his lips and lighting it up. He hesitated for a moment before offering the pack to her.

Audrina stared at it, her heart suddenly skipping a beat. She reached out and took one, leaning towards him as he lit it. She took a careful drag, wrinkling her nose at the vaguely familiar taste of tobacco and nicotine. She’d smoked a few times, mostly socially, in college, but even so, she still coughed a bit.

Miah chuckled a little, exhaling a plume of blue-white smoke himself. “First time?”

“No,” she mumbled, her cheeks heating up a little out of embarrassment. “Been a while.”

“Rebellious,” he said, amused. “I like it.”

Audrina couldn’t hide the smile that comment caused, “S-shut up…”

“Anyway,” Miah continued after a moment. “My dad was the kind of person that just couldn’t handle life, I think. He ended up offing himself when I was sixteen.”

She took a drag and it went down easier this time, exhaling and looking at him in surprise. “S-suicide?” she whispered.

He nodded.

“Jeez,” she let out a breath, “I’m sorry, Miah.”

The blond shook his head, “Don’t be. It’s a selfish thing, suicide. You think you’re keeping others from being burdened by you, but really you’re just leaving behind people that need you.”

She looked at the church again. Her mind wondered what sort of deity would be so cruel to afflict someone’s mind with such torment that their only relief was to no longer exist. “Do you believe? In God, religion, or anything like that?” she asked suddenly.

Miah laughed bitterly, “Hell no. What about you? Did your little school girl pinafore and habit leech your beliefs out of you?” His tease was almost on the mark.

Audrina scoffed, shooting a glare at him. “I wasn’t a nun, jeez,” she grumbled. “But that school certainly didn’t help. I also experienced a lot in college, and that kind of helped me separate the church from reality, as it is.”

“Yeah? Were you the rebellious preacher’s daughter once daddy’s eyes weren’t on you?” he continued his almost-mocking tone, but she could tell there was no malice hidden in his words.

She rolled her eyes, taking another drag and pausing before answering. “I experimented like any other young adult, I suppose. I definitely feel like a fish out of water being back here, though.” she frowned.

“Mm, I can imagine,” he nodded solemnly, finishing his cigarette and stubbing it out in the dirt, flicking the butt away.

Audrina followed its trajectory until it settled on the ground somewhere. She finished hers as well and flicked it away in the same manner. “What about you? How… have you been, after what happened with your dad?” she asked.

Miah took a deep breath, “Better now. I was just a runt back then, so I didn’t have much goin’ for me. Reagan actually found me and took me in. Wouldn’t let me prospect until I was eighteen, though.”

“Prospect? What’s that?”

He glanced at her and smiled, “Right. It’s when you first join a club, like a probationary period. A test trial, if you prefer. Gotta prove your worth and your loyalty to your brothers and all that before you get patched in.”

Audrina took a closer look at his leather jacket, noting the various patches on it, “I’m assuming ‘patched in’ means you get something like that, and you’re official?”

“Yeah. It’s called a cut. We always wear our cuts no matter what,” he explained, and she could hear that hint of pride in his tone.

“You also said ‘brothers’. Is that what you guys are? A brotherhood of sorts?”

“Something like that. It’s hard to explain, but once you’re in the club, you’re in for life. It becomes your life, and the other members, your brothers, are your family. We protect our family, always. We always have each other’s backs,” Miah spoke in a somber tone, and she could tell that this ‘club’ of his was very serious for him.

It made her think of the church again. Always was she making comparisons, but she couldn’t help it. Religion had been her life for so long, and this biker, Miah, was speaking about something that seemed deeper even than the bond the parishioners had with their priest. Almost like a blood oath or something.

Audrina was envious. “It sounds incredible,” her voice was quiet. “That sense of family and loyalty, even if you aren’t actually related… I can’t imagine what that’s like.”

She could feel his brown eyes on her again, watching her. Probably seeing if she was teasing or being mocking. “It is,” he agreed. “Like nothing else.”

She was about to say something else when the old brass bell on top of the church suddenly tolled, its rings clanging out into the still summer heat. It made her flinch and she looked upset that their conversation had to end now.

Together, they stood up and walked back out into the summer sun, now a comfortable warmth rather than a blazing heat on her skin. The door to the church opened and people started leaving, looking red and sweaty and uncomfortable. Audrina crossed her arms over her chest and turned to Miah. “Will I see you again?” she furrowed her brows a little, frowning.

It should have been embarrassing, the almost desperate tone in her voice, but she couldn’t help it. He was like nothing she’d ever seen or experienced before. If anything, he was far more understanding than any of the ‘devout Catholics’ that were currently coming out of their sardine can.

His smile was a warm one, and he reached out to poke her cheek with one of his calloused fingers. “Smile,” he said, and it wasn’t a demand nor a suggestion. “Whatever bad shit is going on, just keep smiling, okay?”

Her breath left her, her blue eyes widening. She could feel her cheeks heat up again. “I…”

Miah reached up to brush his thumb over her eyebrow, where that strawberry birthmark marred her face like a splotch of red paint staining her skin. A mark that her father had said she was kissed by an angel, while Mary Louanne said she was branded by the devil. A mark that she was otherwise self-conscious of until this moment.

“I’ll be around again,” he answered her question, dropping his hand.

“… You promise?” she whispered.

Almost like it was ingrained in her, she could feel her father’s gaze boring into her back, knowing he’d have words for her later. Miah glanced over her shoulder, and she knew who he was looking at without turning around. “Yeah. I promise,” he said softly.

He nodded at her and turned to start walking away. She stood in place and watched him head to the old black Harley that had been parked at the edge of the gravel lot in front of the church. The roar of the motorcycle felt like something ignited inside of her.

“Audrina? Who was that?” her father’s voice sounded huffy and impatient as he strolled up next to her, placing a heavy hand on her shoulder.

Miah turned and shot her a wink before revving his bike and riding away.

“No one important,” she smiled as she lied, knowing she might have just found something—rather, someone—new to put her faith in.

[hyperloopcloud]

Welcome to Hyperloop Cloud Space having 15+ years of experience cloud and data center services! Our expertise in cloud computing and data center management ensures that your business stays ahead in todays fast-paced digital landscape. catering to 12k+ customers our datacenter. They include a range of solutions such as colocation, cloud hosting, disaster recovery, and Multi-Cloud Solutions such as AWS Cloud, Google Cloud, Azure Cloud services.

Hyperloop Cloud computing is characterized by on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. These characteristics enable businesses to access computing resources quickly and easily, adapt to changing demands, and pay only for what they use.

[risunsky]

Hello Risunsky,

I've been following your big traditional art project with great interest and so much enjoyment (the PAIN when the spillage happened in Version 1; big flashbacks to when i poured the brush cleaning water over an almost finished painting...) - almost every update leaves me in awe, truly! I'd totally frame the pencil stage of it already! (the level of detail in your work!! 👌🏽)

As a traditional artist who's always struggling with the restrictions of the scanner, I was wondering how you are going to digitise it? Are you putting it on an A3 scanner bit by bit with turning (and loud cursing) and then putting it together digitally? Or using something like a BookEye Scanner? Or a camera?

This has been on my mind since you presented the new massive sized sheet of paper "HOW is Risunsky gonna scan this monster??"

Anyway, apologies for my curiosity and thank you so much for taking us on this journey, its incredibly interesting, inspiring and helpful to see!!

Have a great evening!

Hello Delullu,

First of all, thank you so much for your kind words, they make me very happy! Your support in sharing and on ko-fi encourages me a lot to keep going!

It's a good question and your curiosity doesn't bother me at all! I still don't know how I'm going to do it, I knew it might be a problem when I started this format but I didn't want it to be a hindrance.

For the moment I have two options: either I find a professional in this field near me and pay for the service, or I use the same method as when I work on A2 format, I scan piece by piece (I only have an A4 scanner) and assemble digitally. It's a rather painful step and I'm apprehensive, but I'll do my best as always.

For this kind of project I tend to jump into the void without a net or elastic and have to improvise at the last minute haha!

[nexustechnoware]

Why AWS is the Best Cloud Hosting Partner for Your Organization – Proven Benefits and Features

More entrepreneurs like e-store owners prefer Amazon Web Services (AWS) for cloud hosting services this year. This article will single out countless reasons to consider this partner for efficient AWS hosting today.

5 Enticing Features of AWS that Make It Perfect for You

The following are the main characteristics of Amazon Web Services (AWS) in 2024.

Scalable

The beauty of AWS is that a client can raise or lower their computing capability based on business demands.

Highly Secure

Secondly, AWS implements countless security measures to ensure the safety of a client’s data. For example, AWS complies with all the set data safety standards to avoid getting lawsuits from disgruntled clients.

Amazon secures all its data centers to ensure no criminal can access them for a nefarious purpose.

Free Calculator

Interestingly, AWS proffers this tool to help new clients get an estimate of the total hosting cost based on their business needs. The business owner only needs to indicate their location, interested services, and their zone.

Pay-As-You-Go Pricing Option

New clients prefer this company for AWS hosting services because this option lets them pay based on the resources they add to this platform.

User-Friendly

AWS is the best hosting platform because it has a user-oriented interface. For example, the provider has multiple navigation links plus instructional videos to enable the clients to use this platform.

Clients can edit updated data whenever they choose or add new company data to their accounts.

Unexpected Advantages of Seeking AWS Hosting Services

Below are the scientific merits of relying on Amazon Web Services (AWS) for web design and cloud computing services.

Relatively Fair Pricing Models

Firstly, the AWS hosting service provider offers well-thought-out pricing options to ensure the client only pays for the resources they utilize. For example, you can get a monthly option if you have many long-term projects.

Limitless Server Capacity

AWS offers a reasonable hosting capacity to each client to enable them to store as much company data as possible. Therefore, this cloud hosting partner ensures that employees can access crucial files to complete activities conveniently.

Upholds Confidentiality

AWS has at least twelve (12) data centers in different parts of the world. Further, this provider’s system is relatively robust and secure to safeguard sensitive clients’ data 24/7.

High-Performance Computing

Unlike other cloud hosting sites, AWS can process meta-data within seconds, enabling employees to meet their daily goals.

Highly Reliable

Unknown to some, over 1M clients in various countries rely on AWS for web development or hosting services. Additionally, AWS is available in over 200 countries spread across different continents.

Finally, AWS’s technical team spares no effort to implement new technologies to safeguard their clients’ data and woo new ones.

Summary

In closing, the beauty of considering this partner for AWS hosting is that it has a simple layout-hence ideal for everyone, including non-techies. Additionally, the fact that this partner is elastic ensures that this system can shrink or expand based on the files you add.

At its core, AWS offers various cloud services, such as storage options, computing power, and networking through advanced technology. NTSPL Hosting offers various features on AWS hosting aimed at improving the scalability of cloud infrastructure for less downtimes. Some of the services NTSPL Hosting offers include pioneering server administration, version control, and system patching. Given that it offers round the clock customer service; it is a good option for those looking for a solid AWS hosting solution.

Source: NTSPL Hosting

[techblog-365]

CLOUD COMPUTING: A CONCEPT OF NEW ERA FOR DATA SCIENCE

Cloud Computing is the most interesting and evolving topic in computing in the recent decade. The concept of storing data or accessing software from another computer that you are not aware of seems to be confusing to many users. Most the people/organizations that use cloud computing on their daily basis claim that they do not understand the subject of cloud computing. But the concept of cloud computing is not as confusing as it sounds. Cloud Computing is a type of service where the computer resources are sent over a network. In simple words, the concept of cloud computing can be compared to the electricity supply that we daily use. We do not have to bother how the electricity is made and transported to our houses or we do not have to worry from where the electricity is coming from, all we do is just use it. The ideology behind the cloud computing is also the same: People/organizations can simply use it. This concept is a huge and major development of the decade in computing.

Cloud computing is a service that is provided to the user who can sit in one location and remotely access the data or software or program applications from another location. Usually, this process is done with the use of a web browser over a network i.e., in most cases over the internet. Nowadays browsers and the internet are easily usable on almost all the devices that people are using these days. If the user wants to access a file in his device and does not have the necessary software to access that file, then the user would take the help of cloud computing to access that file with the help of the internet.

Cloud computing provide over hundreds and thousands of services and one of the most used services of cloud computing is the cloud storage. All these services are accessible to the public throughout the globe and they do not require to have the software on their devices. The general public can access and utilize these services from the cloud with the help of the internet. These services will be free to an extent and then later the users will be billed for further usage. Few of the well-known cloud services that are drop box, Sugar Sync, Amazon Cloud Drive, Google Docs etc.

Finally, that the use of cloud services is not guaranteed let it be because of the technical problems or because the services go out of business. The example they have used is about the Mega upload, a service that was banned and closed by the government of U.S and the FBI for their illegal file sharing allegations. And due to this, they had to delete all the files in their storage and due to which the customers cannot get their files back from the storage.

Service Models Cloud Software as a Service Use the provider's applications running on a cloud infrastructure Accessible from various client devices through thin client interface such as a web browser Consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage

Google Apps, Microsoft Office 365, Petrosoft, Onlive, GT Nexus, Marketo, Casengo, TradeCard, Rally Software, Salesforce, ExactTarget and CallidusCloud

Cloud Platform as a Service Cloud providers deliver a computing platform, typically including operating system, programming language execution environment, database, and web server Application developers can develop and run their software solutions on a cloud platform without the cost and complexity of buying and managing the underlying hardware and software layers

AWS Elastic Beanstalk, Cloud Foundry, Heroku, Force.com, Engine Yard, Mendix, OpenShift, Google App Engine, AppScale, Windows Azure Cloud Services, OrangeScape and Jelastic.

Cloud Infrastructure as a Service Cloud provider offers processing, storage, networks, and other fundamental computing resources Consumer is able to deploy and run arbitrary software, which can include operating systems and applications Amazon EC2, Google Compute Engine, HP Cloud, Joyent, Linode, NaviSite, Rackspace, Windows Azure, ReadySpace Cloud Services, and Internap Agile

Deployment Models Private Cloud: Cloud infrastructure is operated solely for an organization Community Cloud : Shared by several organizations and supports a specific community that has shared concerns Public Cloud: Cloud infrastructure is made available to the general public Hybrid Cloud: Cloud infrastructure is a composition of two or more clouds

Advantages of Cloud Computing • Improved performance • Better performance for large programs • Unlimited storage capacity and computing power • Reduced software costs • Universal document access • Just computer with internet connection is required • Instant software updates • No need to pay for or download an upgrade

Disadvantages of Cloud Computing • Requires a constant Internet connection • Does not work well with low-speed connections • Even with a fast connection, web-based applications can sometimes be slower than accessing a similar software program on your desktop PC • Everything about the program, from the interface to the current document, has to be sent back and forth from your computer to the computers in the cloud

About Rang Technologies: Headquartered in New Jersey, Rang Technologies has dedicated over a decade delivering innovative solutions and best talent to help businesses get the most out of the latest technologies in their digital transformation journey. Read More...

[monisha1199]

Exploring the Power of Amazon Web Services: Top AWS Services You Need to Know

In the ever-evolving realm of cloud computing, Amazon Web Services (AWS) has established itself as an undeniable force to be reckoned with. AWS's vast and diverse array of services has positioned it as a dominant player, catering to the evolving needs of businesses, startups, and individuals worldwide. Its popularity transcends boundaries, making it the preferred choice for a myriad of use cases, from startups launching their first web applications to established enterprises managing complex networks of services. This blog embarks on an exploratory journey into the boundless world of AWS, delving deep into some of its most sought-after and pivotal services.

As the digital landscape continues to expand, understanding these AWS services and their significance is pivotal, whether you're a seasoned cloud expert or someone taking the first steps in your cloud computing journey. Join us as we delve into the intricate web of AWS's top services and discover how they can shape the future of your cloud computing endeavors. From cloud novices to seasoned professionals, the AWS ecosystem holds the keys to innovation and transformation. 

Amazon EC2 (Elastic Compute Cloud): The Foundation of Scalability At the core of AWS's capabilities is Amazon EC2, the Elastic Compute Cloud. EC2 provides resizable compute capacity in the cloud, allowing you to run virtual servers, commonly referred to as instances. These instances serve as the foundation for a multitude of AWS solutions, offering the scalability and flexibility required to meet diverse application and workload demands. Whether you're a startup launching your first web application or an enterprise managing a complex network of services, EC2 ensures that you have the computational resources you need, precisely when you need them.

Amazon S3 (Simple Storage Service): Secure, Scalable, and Cost-Effective Data Storage When it comes to storing and retrieving data, Amazon S3, the Simple Storage Service, stands as an indispensable tool in the AWS arsenal. S3 offers a scalable and highly durable object storage service that is designed for data security and cost-effectiveness. This service is the choice of businesses and individuals for storing a wide range of data, including media files, backups, and data archives. Its flexibility and reliability make it a prime choice for safeguarding your digital assets and ensuring they are readily accessible.

Amazon RDS (Relational Database Service): Streamlined Database Management Database management can be a complex task, but AWS simplifies it with Amazon RDS, the Relational Database Service. RDS automates many common database management tasks, including patching, backups, and scaling. It supports multiple database engines, including popular options like MySQL, PostgreSQL, and SQL Server. This service allows you to focus on your application while AWS handles the underlying database infrastructure. Whether you're building a content management system, an e-commerce platform, or a mobile app, RDS streamlines your database operations.

AWS Lambda: The Era of Serverless Computing Serverless computing has transformed the way applications are built and deployed, and AWS Lambda is at the forefront of this revolution. Lambda is a serverless compute service that enables you to run code without the need for server provisioning or management. It's the perfect solution for building serverless applications, microservices, and automating tasks. The unique pricing model ensures that you pay only for the compute time your code actually uses. This service empowers developers to focus on coding, knowing that AWS will handle the operational complexities behind the scenes.

Amazon DynamoDB: Low Latency, High Scalability NoSQL Database Amazon DynamoDB is a managed NoSQL database service that stands out for its low latency and exceptional scalability. It's a popular choice for applications with variable workloads, such as gaming platforms, IoT solutions, and real-time data processing systems. DynamoDB automatically scales to meet the demands of your applications, ensuring consistent, single-digit millisecond latency at any scale. Whether you're managing user profiles, session data, or real-time analytics, DynamoDB is designed to meet your performance needs.

Amazon VPC (Virtual Private Cloud): Tailored Networking for Security and Control Security and control over your cloud resources are paramount, and Amazon VPC (Virtual Private Cloud) empowers you to create isolated networks within the AWS cloud. This isolation enhances security and control, allowing you to define your network topology, configure routing, and manage access. VPC is the go-to solution for businesses and individuals who require a network environment that mirrors the security and control of traditional on-premises data centers.

Amazon SNS (Simple Notification Service): Seamless Communication Across Channels Effective communication is a cornerstone of modern applications, and Amazon SNS (Simple Notification Service) is designed to facilitate seamless communication across various channels. This fully managed messaging service enables you to send notifications to a distributed set of recipients, whether through email, SMS, or mobile devices. SNS is an essential component of applications that require real-time updates and notifications to keep users informed and engaged.

Amazon SQS (Simple Queue Service): Decoupling for Scalable Applications Decoupling components of a cloud application is crucial for scalability, and Amazon SQS (Simple Queue Service) is a fully managed message queuing service designed for this purpose. It ensures reliable and scalable communication between different parts of your application, helping you create systems that can handle varying workloads efficiently. SQS is a valuable tool for building robust, distributed applications that can adapt to changes in demand.

In the rapidly evolving landscape of cloud computing, Amazon Web Services (AWS) stands as a colossus, offering a diverse array of services that address the ever-evolving needs of businesses, startups, and individuals alike. AWS's popularity transcends industry boundaries, making it the go-to choice for a wide range of use cases, from startups launching their inaugural web applications to established enterprises managing intricate networks of services.

To unlock the full potential of these AWS services, gaining comprehensive knowledge and hands-on experience is key. ACTE Technologies, a renowned training provider, offers specialized AWS training programs designed to provide practical skills and in-depth understanding. These programs equip you with the tools needed to navigate and excel in the dynamic world of cloud computing.

With AWS services at your disposal, the possibilities are endless, and innovation knows no bounds. Join the ever-growing community of cloud professionals and enthusiasts, and empower yourself to shape the future of the digital landscape. ACTE Technologies is your trusted guide on this journey, providing the knowledge and support needed to thrive in the world of AWS and cloud computing.

[annajade456]

DevOps for Beginners: Navigating the Learning Landscape

DevOps, a revolutionary approach in the software industry, bridges the gap between development and operations by emphasizing collaboration and automation. For beginners, entering the world of DevOps might seem like a daunting task, but it doesn't have to be. In this blog, we'll provide you with a step-by-step guide to learn DevOps, from understanding its core philosophy to gaining hands-on experience with essential tools and cloud platforms. By the end of this journey, you'll be well on your way to mastering the art of DevOps.

The Beginner's Path to DevOps Mastery:

1. Grasp the DevOps Philosophy:

Start with the Basics: DevOps is more than just a set of tools; it's a cultural shift in how software development and IT operations work together. Begin your journey by understanding the fundamental principles of DevOps, which include collaboration, automation, and delivering value to customers.

2. Get to Know Key DevOps Tools:

Version Control: One of the first steps in DevOps is learning about version control systems like Git. These tools help you track changes in code, collaborate with team members, and manage code repositories effectively.

Continuous Integration/Continuous Deployment (CI/CD): Dive into CI/CD tools like Jenkins and GitLab CI. These tools automate the building and deployment of software, ensuring a smooth and efficient development pipeline.

Configuration Management: Gain proficiency in configuration management tools such as Ansible, Puppet, or Chef. These tools automate server provisioning and configuration, allowing for consistent and reliable infrastructure management.

Containerization and Orchestration: Explore containerization using Docker and container orchestration with Kubernetes. These technologies are integral to managing and scaling applications in a DevOps environment.

3. Learn Scripting and Coding:

Scripting Languages: DevOps engineers often use scripting languages such as Python, Ruby, or Bash to automate tasks and configure systems. Learning the basics of one or more of these languages is crucial.

Infrastructure as Code (IaC): Delve into Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation. IaC allows you to define and provision infrastructure using code, streamlining resource management.

4. Build Skills in Cloud Services:

Cloud Platforms: Learn about the main cloud providers, such as AWS, Azure, or Google Cloud. Discover the creation, configuration, and management of cloud resources. These skills are essential as DevOps often involves deploying and managing applications in the cloud.

DevOps in the Cloud: Explore how DevOps practices can be applied within a cloud environment. Utilize services like AWS Elastic Beanstalk or Azure DevOps for automated application deployments, scaling, and management.

5. Gain Hands-On Experience:

Personal Projects: Put your knowledge to the test by working on personal projects. Create a small web application, set up a CI/CD pipeline for it, or automate server configurations. Hands-on practice is invaluable for gaining real-world experience.

Open Source Contributions: Participate in open source DevOps initiatives. Collaborating with experienced professionals and contributing to real-world projects can accelerate your learning and provide insights into industry best practices.

6. Enroll in DevOps Courses:

Structured Learning: Consider enrolling in DevOps courses or training programs to ensure a structured learning experience. Institutions like ACTE Technologies offer comprehensive DevOps training programs designed to provide hands-on experience and real-world examples. These courses cater to beginners and advanced learners, ensuring you acquire practical skills in DevOps.

In your quest to master the art of DevOps, structured training can be a game-changer. ACTE Technologies, a renowned training institution, offers comprehensive DevOps training programs that cater to learners at all levels. Whether you're starting from scratch or enhancing your existing skills, ACTE Technologies can guide you efficiently and effectively in your DevOps journey. DevOps is a transformative approach in the world of software development, and it's accessible to beginners with the right roadmap. By understanding its core philosophy, exploring key tools, gaining hands-on experience, and considering structured training, you can embark on a rewarding journey to master DevOps and become an invaluable asset in the tech industry.

[infosectrain03]

Amazon Elastic Block Store, commonly known as EBS, is a significant component of Amazon Web Services (AWS) storage services, offering a versatile and reliable solution for managing data storage in the cloud. EBS allows users to create and attach block storage volumes to Amazon Elastic Compute Cloud (EC2) instances, making it an integral part of building scalable and high-performance applications on the AWS platform.

[seidormena]

Optimizing SAP Workloads on AWS Cloud

Migrating SAP workloads to the cloud has become a key strategy for companies looking to increase agility, reduce costs, and improve scalability. AWS Cloud offers an ideal environment for running critical enterprise applications like SAP, providing secure, flexible, and high-performance infrastructure.

By migrating SAP workloads to AWS, companies can experience a range of benefits, including:

Scalability and Elasticity: AWS enables quick and easy resource scaling to adapt to changing business demands, ensuring optimal performance at all times.

Advanced Security: AWS infrastructure is designed to meet the most stringent security standards, helping to protect sensitive data and mitigate security risks.

Cost Reduction: By leveraging flexible pricing models and resource optimization, companies can significantly reduce operational costs associated with running SAP workloads in the cloud.

High Availability and Business Continuity: AWS offers a wide range of services to ensure high availability and business continuity, including data replication, disaster recovery, and automatic failover capability.

In summary, running SAP workloads on the AWS cloud can provide companies with the agility, security, and operational efficiency needed to drive business success in a digitally transformed world.