#Amazon EC2

[cyber-techs]

Deploying NAKIVO Backup & Replication as an AMI in Amazon EC2 for Cloud Backups

As more businesses shift to the cloud, having a solid backup plan is essential. If you're responsible for managing company data, you know how critical it is to ensure that your backups are not only reliable but also easy to manage. That's where NAKIVO Backup & Replication comes in, offering a comprehensive solution to protect your data—whether it’s stored on virtual machines, physical servers, or in the cloud.

Deploying NAKIVO Backup & Replication as an Amazon Machine Image (AMI) in Amazon EC2 combines the power of AWS with the simplicity of NAKIVO, giving you a scalable, cost-effective way to back up your data. This guide will walk you through the process step by step, and explain why this setup might be perfect for your business.

Why Choose NAKIVO Backup & Replication on Amazon EC2?

Let’s start with why you’d want to deploy NAKIVO Backup & Replication in Amazon EC2. Simply put, EC2 gives you flexible, scalable computing resources in the cloud, which makes it perfect for handling data backups. Combine that with NAKIVO’s user-friendly interface and powerful features, and you’ve got a cloud-based backup solution that grows with your business.

Here’s what makes this setup a good choice:

Scalability: As your business grows, so does your data. Amazon EC2 makes it easy to add more computing power as needed, without having to worry about buying new hardware.

Cost Efficiency: With AWS, you only pay for what you use. This means you can run your backup solution without breaking the bank.

Automation: NAKIVO lets you automate your backups, so you can schedule them and let the system do the work—no more manually backing up data.

Security: AWS is known for its strong security features, and NAKIVO adds another layer of protection, giving you peace of mind that your data is safe.

Now, let’s dive into how to deploy NAKIVO Backup & Replication as an AMI on Amazon EC2.

Step 1: Launch an Amazon EC2 Instance

To get started, you’ll first need to launch an Amazon EC2 instance. This is the virtual machine that will host your NAKIVO Backup & Replication software.

Log into AWS Console: If you don’t already have an AWS account, you’ll need to create one. Once you’ve logged in, head to the EC2 Dashboard.

Launch a New EC2 Instance:

Click on Launch Instance to start setting up your virtual machine.

Search for NAKIVO Backup & Replication in the AMI section. If it’s not available, you can download the NAKIVO installer separately and use any compatible AMI to start.

Choose an instance type. For many businesses, a t3.medium instance is a good starting point. But if you have a lot of data or need more computing power, you can select a larger instance.

Configure the Instance:

Set up your network and security options. Make sure you allocate enough storage for your backups, as this is where all your data will be stored.

If you need a static IP address for easy access, assign an Elastic IP.

Launch the Instance: After you’ve completed the configuration, click Launch. You’ll need to create or select an existing key pair to securely access the instance.

Step 2: Install and Set Up NAKIVO Backup & Replication

Once your EC2 instance is up and running, the next step is to install and configure NAKIVO Backup & Replication.

Access Your Instance: To access the instance, use an SSH client (like PuTTY) and connect using the public IP address of your instance along with the key pair you created during the setup.

Install NAKIVO Backup & Replication:

If the AMI you selected already includes NAKIVO, you’re good to go! If not, download the installer from the NAKIVO website and follow the installation steps to get everything set up.

Once installed, open your web browser and log in to the NAKIVO interface by entering the IP address of your EC2 instance.

Initial Configuration: When you’re logged in to NAKIVO, you’ll need to set up the backup solution to fit your needs. Choose what data you want to back up—whether it’s virtual machines, physical servers, or cloud workloads—and select where you want the backups to be stored. If you’re using Amazon S3 for storage, you can configure that here as well.

Step 3: Automate and Schedule Backups

One of the best features of NAKIVO Backup & Replication is its ability to automate backups, which means you can set it and forget it.

Create Backup Jobs: From the NAKIVO dashboard, set up backup jobs for your data. You can customize these jobs based on your needs—daily backups, weekly backups, or however often you want them to run.

Set Retention Policies: To help manage storage space, you can create retention policies. These determine how long old backups are kept and when they are automatically deleted to free up space.

Enable Notifications: You can configure email alerts to notify you if something goes wrong with a backup. This way, you’re always in the loop if an issue arises.

Optimize Storage with Amazon S3: Using Amazon S3 for backup storage gives you a scalable, cost-effective way to keep your data safe. NAKIVO integrates easily with S3, making it simple to back up and store your data securely.

Step 4: Test Your Backups and Recovery Process

With everything set up, it’s important to test your backup and recovery process to make sure everything works as expected.

Run a Test Backup: Select a small workload or non-critical data to test the backup process. Check that the data is successfully backed up to your storage location.

Test Data Recovery: Having a backup is great, but you need to be sure you can restore your data when you need it. Run a recovery test to confirm that the recovery process works smoothly.

Monitor Performance: Watch how your EC2 instance and NAKIVO perform. If you find that backups are running slow or using too many resources, you might need to upgrade your instance to handle larger workloads.

Step 5: Scale and Maintain as You Grow

As your business grows, so will your data. Fortunately, NAKIVO Backup & Replication and Amazon EC2 are built to scale.

Scale Your Instance: If you’re outgrowing your current setup, it’s easy to scale up your EC2 instance. You can switch to a more powerful instance with just a few clicks, giving you the resources you need to handle more backups.

Add Storage with Amazon S3: If your data is growing quickly, you can increase your storage by adding more capacity with Amazon S3. This way, you’ll never have to worry about running out of space.

Keep Software Updated: Make sure to keep NAKIVO Backup & Replication up to date. This will ensure you’re getting the latest features and security patches.

[webstryker]

What is Amazon EC2? What are the Benefits, Types, and Different Pricing Models of AWS EC2?

Did you know that Amazon EC2 (Elastic Compute Cloud) powers more than a million active customers across 245 countries and territories, making it one of the most popular cloud services in the world?

This comprehensive guide is for advanced users, DevOps professionals, beginners, and engineers who are looking to harness the power of AWS EC2 to enhance their cloud infrastructure.

[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…

[govindhtech]

Amazon EC2 M7i Instances Hypercharge Cloud AI

Amazon EC2 M7i Instances 

Intel would like to demonstrate how the latest Amazon EC2 M7i Instances and M7i-flex instances with 4th Generation Intel Xeon Scalable processors can support your AI, ML, and DL workloads in this second of a three-blog series. They explained these new instances and their broad benefits in the first blog. They wanted to know how AI, ML, and DL workloads perform on these new instances and how Intel CPUs may help.

One research assesses the AI industry at $136.55 billion USD and predicts 37.3% yearly growth until 2030. While you could credit the increase to apparent AI usage like Google and Tesla’s self-driving cars, the advertising and media sector dominates the worldwide AI market. AI and ML/DL workloads are everywhere and growing. Cloud service providers (CSPs) like Amazon Web Services (AWS) are investing in AI/ML/DL services and infrastructure to enable organizations adopt these workloads more readily. Hosting instances with 4th-generation Intel Xeon Scalable CPUs and AI accelerators is one investment.

This article will explain how Intel CPUs and AWS instances are ideal for AI workloads. Two typical ML/DL model types will be used to demonstrate how these instances performed these workloads.

Amazon EC2 M7i &M7i Flex with 4th Gen Intel Xeon Scalables

As mentioned in the previous blog, Amazon EC2 provides M7i and M7i-flex with the newest Intel Xeon CPU. Primary difference: M7i-flex offers changeable performance at a reduced price. This blog will concentrate on regular Amazon EC2 M7i instances for sustained, compute-intensive applications like training or executing machine learning models. M7i instances have 2–192 vCPUs for various requirements. Each instance may accommodate up to 128 EBS disks, providing ample of storage for your dataset. The newest Intel Xeon processors include various built-in accelerators to boost task performance.

For better deep learning performance, all Amazon EC2 M7i instances include Intel Advanced Matrix Extensions (AMX) accelerator. Intel AMX lets customers code AI tasks on the AMX instruction set while keeping non-AI workloads on the CPU ISA. Intel has optimized its oneAPI Deep Neural Network Library (oneDNN) to make AMX easier to use for developers. Open-source AI frameworks like PyTorch, TensorFlow, and ONYX support this API. Intel tested 4th Gen Intel Xeon Scalable processors with AMX capabilities to give 10 times the inference performance of earlier CPUs.

Engineers and developers must adjust their AI, ML, and DL workloads on the newest Amazon EC2 M7i instances with Intel AMX to maximize performance. Intel offers an AI tuning guide to take use of Intel processor benefits across numerous popular models and frameworks. OS-level optimizations, PyTorch, TensorFlow, OpenVINO, and other optimizations are covered throughout the guide. The Intel Model Zoo GitHub site contains pre-trained AI, ML, and DL models pre-validated for Intel hardware, AI workload optimization guidance, best practices, and more.

After learning how Intel and the newest Intel Xeon processors may better AI, ML, and DL workloads, let’s see how these instances perform with object identification and natural language processing.

Models for detecting objects

Object detection models control image-classification applications. This category includes 3D medical scan, self-driving car camera, face recognition, and other models. They will discuss ResNet-50 and RetinaNet.

A 50-layer CNN powers ResNet-50, an image recognition deep learning model. User-trained models identify and categorize picture objects. ResNet-50 models on Intel Model Zoo and others train using ImageNet’s big picture collection. Most object identification models have one or two stages, with two-stage models being more accurate but slower. ResNet-50 and RetinaNet are single-stage models, although RetinaNet’s Focal Loss function improves accuracy without losing speed.

Performance how rapidly these models process photos depends on their use. End consumers don’t want lengthy waits for device recognition and unlocking. Before plant diseases and insect incursions damage crops, farmers must discover them immediately. Intel’s MLPerf RetinaNet model demonstrates that Amazon EC2 M7i instances analyze 4.11 times more samples per second than M6i instances.

As CPUs rise, ResNet-50 performance scales nicely, so you can retain high performance independent of dataset and instance size. An Amazon EC2 M7i instance with 192 vCPUs has eight times the ResNet-50 throughput of a 16vCPU instance. Higher-performing instances provide better value. Amazon EC2 M7i instances analyzed 4.49 times more samples per dollar than M6i instances in RetinaNet testing. These findings demonstrate that Amazon EC2 M7i instances with 4th Gen Intel Xeon Scalable CPUs are ideal for object identification deep learning tasks.

Natural Language Models

You’re probably using natural language processing engines when you ask a search engine or chatbot a query. NLP models learn real speech patterns to comprehend and interact with language. BERT machine learning models can interpret and contextualize text in addition to storing and presenting it. Word processing and phone messaging applications now forecast content based on what users have typed. Small firms benefit from chat boxes for first consumer contacts, even if they don’t run Google Search. These firms require a clear, fast, accurate chatbot.

Chatbots and other NLP model applications demand real-time execution, therefore speed is crucial. With Amazon EC2 M7i instances and 4th Generation Intel Xeon processors, NLP models like BERT and RoBERTa, an optimized BERT, perform better. One benchmark test found that Amazon EC2 M7i instances running RoBERTa analyzed 10.65 times more phrases per second than Graviton-based M7g instances with the same vCPU count. BERT testing with the MLPerf suite showed that throughput scaled well when they raised the vCPU count of Amazon EC2 M7i instances, with the 192-vCPU instance attaining almost 4 times the throughput of the 32-vCPU instance.

The Intel AMX accelerator in the 4th Gen Intel Xeon Scalable CPUs helps the Amazon EC2 M7i instances function well. Intel gives clients everything they need to improve NLP workloads with publicly accessible pre-optimized Intel processor models and tuning instructions for particular models like BERT. Amazon EC2 M7i instances outperformed M7g instances by 8.62 times per dollar, as RetinaNet showed.

Conclusion

For AI, ML, and DL, cloud decision-makers should use Amazon EC2 M7i instances with 4th Generation Intel Xeon Scalable CPUs. These instances include Intel AMX acceleration, tuning guidelines, and optimized models for many typical ML applications, delivering up to 10 times the throughput of Graviton-based M7g instances. Watch for further articles on how the newest Amazon EC2 M7i and M7i-flex instances may serve different workloads.

Read more on Govindhtech.com

[aravikumar48]

Lambda Lab | Start and Stop EC2 Instances using Lambda Function | Tech A...

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[muellermh]

Welche AWS-Services stehen für Cloud Computing zur Verfügung?: Die Überschrift lautet: "Cloud Computing mit AWS: Die verschiedenen Services im Überblick"

#CloudComputing #AWS #AmazonEC2 #AmazonS3 #AmazonRDS #AWSElasticBeanstalk #AWSLambda #AmazonRedshift #AmazonKinesis #AmazonECS #AmazonLightsail #AWSFargate Entdecken Sie die verschiedenen Services von AWS für Cloud Computing und machen Sie sich mit den Vor- und Nachteilen vertraut!

Cloud Computing ist in vielerlei Hinsicht eine revolutionäre Technologie. Es bietet Unternehmen die Möglichkeit, ihr Rechenzentrum zu einer kostengünstigen, zuverlässigen und flexiblen Infrastruktur zu machen. Mit Cloud Computing können Unternehmen auf jeder Plattform, zu jeder Zeit und an jedem Ort auf ihre Rechenzentrumsressourcen zugreifen. Amazon Web Services (AWS) ist einer der führenden…

[indian-school-of-ethical-hacking]

Amazon EC2 is a web service offered by AWS that provides resizable computing capacity in the cloud that can be used for a wide range of computing workloads.

[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.

[ajpandey1]

Amazon Web Service & Adobe Experience Manager:- A Journey together (Part-6)

In the previous parts (1,2,3,4 & 5) we discussed how one day digital market leader meet with the a friend AWS in the Cloud and become very popular pair. It bring a lot of gifts for the digital marketing persons. Then we started a journey into digital market leader house basement and structure, mainly repository CRX and the way its MK organized. Ways how both can live and what smaller modules they used to give architectural benefits.Also visited how they are structured together to give more.

In this part as well will see more on the eCommerce side of this combination.

Before going into this section of house , we want to go with eCommerce and content chemistry.

We got one picture in the house presenting AEM capabilities as content required in eCommerce.

Enhanced shopping experience with Adobe Commerce

Adobe Commerce, companies can integrate digital and physical shopping experiences

Best-in-class cloud-based omnichannel solutions, including in-store, retail associate, and order management technologies.

Adobe Commerce increases the rate you can provision your e-commerce to scale globally. It also offers flexibility for B2C and B2B experiences built on a headless, extensible architecture.

Adobe Commerce’s extensibility platform

Adobe Commerce’s extensibility platform and AWS services—such as Amazon Personalize, Amazon Connect, and Analytics on AWS — deliver omnichannel fulfillment and self-service.

Campaign Management Across Multichannel

Amazon AppFlow with Marketo Engage to securely transfer data between AWS services and cloud applications.

The fully-managed integration enables organization to expand the features of Marketo Engage. Organization select sales Leads and Activities Objects in Marketo and move them into AWS Services e.g.

Amazon S3, Amazon Redshift, or Amazon Lookout for Metrics.

Organization can also sync objects with CRM, Snowflake, and Upsolver and create new Leads objects in Marketo based on this data.

Adobe Experience Platform can generate value with data stored in AWS Analytics solutions. Data can ingest from various sources like Adobe applications, cloud-based storage, databases, etc.

Adobe Workfront :Marketing Process Optimizer

Adobe Workfront and AWS can serve as the blueprints for a marketing project, starting from campaign completion to process optimization.

Adobe Workfront delivers a 285% return on investment (ROI) for enterprise organization.Adobe Workfront and AWS provide an enterprise-grade, cloud-based work management platform that enables marketers to plan, predict, collaborate, evolve, and deliver their best work.

All necessary metadata is retained for real-time cataloging in Adobe Workfront, so campaign teams can streamline processes while maintaining visibility across all stages of the project development process.

Workfront Fusion: Workflows Automation way

Adobe Workfront Fusion is a powerful integration platform that quickly connects organization business-critical applications– with or without those built on AWS Cloud–by automating workflows and processes. This increase focus on core business solution without engaging developers into integrations or provide ongoing maintenance and engineering support.

Adobe Workfront Fusion, data and assets can flow freely across systems and teams in either direction to boost productivity.

Above steps summary:-

Adobe Photoshop:-Content editing process is complete then project status is updated in the Adobe Workfront

Workfront Fusion, Trigger a workflow to route the finished assets to relevant business applications, such as applications built on AWS cloud -Amazon S3, Lambda.

Amazon S3 events: Trigger an AWS Lambda runtime to resize the image.

The workflow then updates the finished content on the web, mobile, or tablet etc.

In this journey we walk through AEM & AWS for Adobe eCommerce to provide solutions to transform your business digitally. So any eCommerce solution deliver holistic, personalized experiences at scale, tailoring each moment of your eCommerce journey.

For more details on this interesting Journey you can browse back earlier parts from 1-5.

[itsneowriting]

Dawn of the Dark Knight

"The Bat signal has been up for the last 15 minutes. Why didn't we get the alert?" Bruce was not happy about the delay. This signal is lit up only in the worst scenarios, and in such cases, every second counts.

"I am not sure, Master Wayne. I'll investigate that while you're away meeting Commissioner Gordon," Alfred said as he pulled up the logs for the Bat signal notification system. "Make sure we are not hacked. That's the last thing I want." Batman left for the Batcave in his Batmobile.

"You're late. I hope everything is alright?" Commissioner Gordon expressed concern since Batman was running late.

"Nothing for you to be concerned about," Batman said in his heavy voice. "Tell me why you summoned me."

"It's Joker again. He's kidnapped the mayor. He wants us to release Harley Quinn from Arkham Asylum," Commissioner Gordon explained, his expression reflecting concern.

Batman checked his Batwatch for the live feed on Harley Quinn, but to his dismay, the data did not load. "Strange," he murmured.

"Everything okay?" Gordon was getting anxious from Batman's expression.

"You needn't worry about me. Tell me what information we have thus far," Batman replied, refocusing the conversation.

"We have limited leads on Joker's location. He expects us to bring Quinn to Gotham Port. And..." Gordon paused, awaiting Batman's response.

"And what?" Batman inquired, still grappling with the malfunctioning Batwatch feed.

"He expects you to bring her there. He said he will release the mayor once he sees both of you at the Northern block of Gotham Port."

"No issues. I'll handle that. I'll meet you at the Asylum. Just tell me Harley's room." Batman assured Gordon.

Gordon went through the papers he held in his folder and read out, "Cell number 234, 8th floor, 3rd Block." As he lifted his face up after reading Harley's records, Batman was nowhere to be seen. Despite having seen the act a thousand times already, Gordon was still impressed.

Jailbreaking Quinn from the Asylum wasn't something that Batman was inexperienced with. In an hour, Batman was en route to Gotham Port with an unconscious Harley on the Batmobile.

"Alfred, what's going on with the Batwatch? The feed isn't updating properly, and our BatCall app is malfunctioning,” Bruce asked Alfred over Facetime, as the BatCall wasn't functioning properly.

"I'm uncertain, sir. I've thoroughly examined the logs, and it doesn't appear that we are being hacked. However, I'm investigating other possible issues," Alfred responded diligently.

"Damn it, Alfred."

"Sir, if I may, where are you headed? I haven’t been able to track the Batmobile’s location since you left."

"Perfect. So, the whole Bat Software system is down now? And we are clueless about the cause. We'll address this once I get back. Tell Damian that I will be in the Northern block. I just picked up Harley Quinn from the Asylum and I am off to Gotham Port."

"Harley Quinn? May I inquire why you have her with you at this moment, Master Wayne?"

"Long story. The mayor has been kidnapped by the Joker and I need to confront him. Tell Damian to wait for my arrival before taking any action."

"Will do, sir."

Bruce ended the call. He was a lot more concerned about how the Bat Software System was not working properly. Has the Joker hacked his way into his system?

"You're late, Batman... Wahahahahah!" The Joker's laughter echoed through the Northern block of Gotham port, laced with annoyance.

"I have Quinn with me. Now where's the mayor?" Batman dismissed Joker's comment, focusing on the task at hand.

"He's at the DA's office. I don’t care about him or her. The only person who matters to me is you. Now let's dance. Wahahahaha." Joker laughed menacingly as he threw a couple of grenades at Batman and the unconscious Harley.

"I forgot that you haven't been to the Asylum in a while. Let's fix that today," said Batman as he swiftly used the BatHook to manoeuvre. Placing Harley in a secure location, he returned to face Joker.

"Not bad, Batman. Though, you are slacking off lately. Now, why don't we spice it up a little?" said Joker as he released a mysterious gas from cylinders hidden around the Northern block. "Damn it. The Batwatch would've picked this up right when we arrived. Had it been working, that is..." Batman thought, realizing the extent of the system malfunction.

Batman felt suffocation and anxiety as he trembled, inhaling the gas. The memory of his parents getting shot came right in front of him.

"Now, Batman, let's settle this for once and for all," Joker took the knife he crafted from the Batarang and held it under Batman's neck. Batman felt too paralyzed to do anything.

"Bang!" A shot was heard before Joker collapsed to the ground.

"Take that, you psycho…" Damian, the Robin, glided down from one of the empty ships surrounding them after he shot down Joker using the tranquilizer.

"Aah the bot wonder. You got the moves alright… Guess you won this time, Batman... Wahah..." Joker fainted, before he could finish his laughter.

"Here, an antidote for Scarecrow's gas." Damian injected the antidote into Batman. "You're late. A couple more seconds, and you would’ve become the next Batman, on my vacancy." Batman said, as he got up, regaining his composure.

"Yeah, couldn't locate you since our entire software systems are down," he replied, acknowledging the complications they faced.

"Understandable. Let's sort this mess first, before digging onto that."

Soon, Joker and Quinn were transferred back to the Asylum, and the day was safe again in Gotham, thanks to the efforts of Batman and the team.

"Alfred, have you figured out the issue with our entire software system going down?" Bruce asked furiously as the setback on the last case still haunted him.

"I'm afraid, yes, Master Wayne," Alfred said as he showed Bruce an email.

"It turns out that Damian accidentally left an EC2 instance running on our AWS account for a very long time. All our Bat Software systems deployed in AWS will no longer function until we pay the bills."

"Well then, pay and close it, Alfred! Don't tell me there's something else."

"I'm afraid there's more. The bill exceeds your bank balance, unfortunately, and they have debited whatever was present in your account."

"Wtf?"

"I'm afraid you are broke, sir."

"F*ck Jeff Bezos!!!" Bruce yelled as he realized this is the end of his story as Batman, and probably Bruce Wayne's as well. Jeff Bezos had accomplished what the Joker never could.

Nb: This short fan fiction was written based on the following prompts:

Part 1

Pick your favorite fairytale/ superhero character & write a short piece. Make sure that the piece is suited to be read to & understood by a 10-year-old child.

Part 2

From where you have stopped, twist the tale in to an adult one.

Pointer - The original fairytales were quite grizzly, gory and twisted. Grimms took those & significantly toned it down to suit the young ones.

[webstryker]

How to set up a scalable web server on AWS EC2 with auto-scaling Did you know that by 2025, the global cloud computing market is projected to reach a staggering $623.3 billion? With the increasing demand for cloud services, it's crucial for businesses to optimize their infrastructure for scalability and reliability. One of the fundamental aspects of achieving this is setting up a scalable web server. In this comprehensive guide, we'll dive deep into the process of setting up a scalable web server on AWS EC2 with auto-scaling.

👉 Scalable Web Server Setup on AWS EC2 with Auto-Scaling: Required Resources, Benefits, Tips & Strategies

[techdirectarchive]

Deep Dive into Protecting AWS EC2, RDS Instances and VPC

Veeam Backup for Amazon Web Services (Veeam Backup for AWS or VBAWS) protects and facilitates disaster recovery for Amazon Elastic Compute Cloud (EC2), Amazon Relational Database Service (RDS), Amazon DynamoDB, and Amazon Elastic File System (EFS) environments. In this article, we shall deep dive into Protecting AWS EC2 and RDS Instances and VPC (Amazon Virtual Private Cloud) configurations as…

[govindhtech]

Qualcomm Cloud AI 100 Lifts AWS’s Latest EC2!

Qualcomm Cloud AI 100 in AWS EC2

With the general release of new Amazon Elastic Compute Cloud (Amazon EC2) DL2q instances, the Qualcomm Cloud AI 100 launch, which built on the company’s technological collaboration with AWS, marked the first significant milestone in the joint efforts. The first instances of the Qualcomm artificial intelligence (AI) solution to be deployed in the cloud are the Amazon EC2 DL2q instances.

The Qualcomm Cloud AI 100 accelerator’s multi-core architecture is both scalable and flexible, making it suitable for a broad variety of use-cases, including:

Large Language Models (LLMs) and Generative AI: Supporting models with up to 16B parameters on a single card and 8x that in a single DL2q instance, LLMs address use cases related to creativity and productivity.

Classic AI: This includes computer vision and natural language processing.

They recently showcased a variety of applications using AWS EC DL2q powered by Qualcomm Cloud AI 100 at this year’s AWS re:Invent 2023:

A conversational AI that makes use of the Llama2 7B parameter LLM model.

Using the Stable Diffusion model, create images from text.

Utilising the Whisper Lite model to simultaneously transcribing multiple audio streams.

Utilising the transformer-based Opus mode to translate between several languages.

Nakul Duggal, SVP & GM, Automotive & Cloud Computing at Qualcomm Technologies, Inc., stated, “Working with AWS is empowering us to build on they established industry leadership in high-performance, low-power deep learning inference acceleration technology.” The work they have done so far shows how well cloud technologies can be integrated into software development and deployment cycles.

An affordable revolution in AI

EC2 customers can run inference on a variety of models with best-in-class performance-per-total cost of ownership (TCO) thanks to the Amazon EC2 DL2q instance. As an illustration:

For DL inference models, there is a price-performance advantage of up to 50% when compared to the latest generation of GPU-based Amazon EC2 instances.

With CV-based security, there is a reduction in Inference cards of over three times, resulting in a significantly more affordable system solution.

allowing for the optimization of 2.5 smaller models, such as Deci.ai models, on Qualcomm Cloud AI 100.

The Qualcomm AI Stack, which offers a consistent developer experience across Qualcomm AI in the cloud and other Qualcomm products, is a feature of the DL2q instance.

The DL2q instances and Qualcomm edge devices are powered by the same Qualcomm AI Stack and base AI technology, giving users a consistent developer experience with a single application programming interface (API) across their:

Cloud,

Automobile,

Computer,

Expanded reality, as well as

Environments for developing smartphones.

Customers can use the AWS Deep Learning AMI (DLAMI), which includes popular machine learning frameworks like PyTorch and TensorFlow along with Qualcomm’s SDK prepackaged.

Read more on Govindhtech.com

[disaster-recovery]

What is AWS Aurora?

When information is gathered, stored and recalled in computing, it is done so via databases. A database is simply an organized collection of data that can be recalled as needed. A database typically does minimal processing outside organizing data, but some databases parse data at a low level. These types of systems are used to collect information like customer billing details or product inventory levels, but they are also used in apps and other software settings where information needs to be stored and recalled to use features of an app or piece of software.

Amazon Web Services (AWS) provides access to cloud-based database engines, and you can turn to AWS for different types of database setups that cater to different needs. If you’re looking for a relational database engine that can integrate with MySQL, AWS Aurora is a good option.

Relational Database Systems

AWS Aurora is part of AWS relational database systems (RDS). This means that databases that take advantage of AWS Aurora are simple to set up and manage in the cloud. Aurora restore backup systems can also be used to protect sensitive data against threats like theft, deletion or corruption. Amazon already provides plenty of security oversight when it comes to database protection, but you can also implement Aurora restore backup services in addition to those offered directly through AWS.

Other Features of AWS Aurora

AWS Aurora also provides access to features like one-way replication and push-button migration. Additionally, you can gain more power from using AWS Aurora through DB clusters. These arrangements harness the power of multiple units working in concert with one another to serve data at scale for larger operations.

Within different regions, AWS Aurora can be configured to include various availability zones. This offers more options to maintain data integrity while also offering speed and enhanced connectivity options. The whole point of having a cluster is to allow data to be served faster to users in different parts of the country or the world, and AWS Aurora can do this when configured according to region and availability zone.

Read a similar article about AWS data protection here at this page.