VMware has announced significant changes to its certification program, eliminating prerequisites for certifications, increased flexibility, and a standardized fee structure for learners. These updates, effective May 6, 2024, cater to the evolving needs of IT professionals and streamline the certification process.

Traditionally, VMware certifications required completing specific training courses or holding prerequisite certifications. Under the new program, candidates can now take exams directly, demonstrating their knowledge and skills without mandated coursework. This allows for a more personalized approach to learning, where professionals can leverage their existing experience and preferred resources to prepare for exams.

Flat Fee for All Exams

VMware has introduced a flat fee of USD $250 for all VCTA, VCP, and VCAP exams. This simplifies the cost structure and ensures transparency for learners. The new fee applies to all new registrations, renewals, and retakes, effective May 6, 2024.

Updated Recognition for Certified Professionals.

While the recent changes to the VMware certification program don’t directly create completely new ways to recognize certified professionals, s

New Certification Version Badges

This update focuses on distinguishing between persons who received certifications through the traditional process (coursework and exam) and those who acquired them exclusively through the exam. These new badges help employers gain a better understanding of a candidate’s learning path and dedication to continual improvement. This could lead to:Recognition of deep knowledge: Employers who seek a comprehensive learning approach may prefer applicants with traditional certification paths reflected in their badges.Highlighting dedication: The new badges might demonstrate a candidate’s initiative in independently gaining the necessary knowledge for the exam.

Also Read: How to Migrate VMware to Hyper-V with Vinchin Backup & Recovery

Enhanced Credibility

The emphasis on certifying abilities through tests, independent of the learning path, improves the overall legitimacy of VMware credentials. This could lead to:

Employers can be more certain that certified professionals can flourish in their roles.

A recognized and approved certification can give VMware professionals an advantage in the employment market.

VMware certificates are widely acknowledged in the IT business, especially among professionals working with virtualization technology. The program modifications reinforce this acknowledgment by:

The emphasis on skill validation ensures that certifications continue to reflect a high level of competency.

VMware’s certifications are regularly updated to reflect the most recent advances in cloud technology, ensuring their continued relevance in the employment market.

Benefits for IT professionals

IT professionals who want to improve their skills, get certified, and move up in their careers in the rapidly changing fields of virtualization and cloud technologies can benefit from the updated VMware certification program in several ways. Here is a list of the main benefits:

Goodbye Prerequisites: There are no more mandatory training classes. You can now jump right into the exam that matches your desired skill set, allowing you to learn at your own speed, focus on your strengths, and get certified faster.

Simplified Cost Structure: The program establishes a fixed cost of $250 for all VCTA, VCP, and VCAP exams, independent of the track. This offers:

Cost Predictability: You can easily budget for your certification aspirations without worrying about directing exam expenses.

Increased accessibility: The consistent charge eliminates a potential financial barrier, making certification more accessible to a broader spectrum of IT professionals.

Enhanced credibility and recognition:

Focus on Skills Validation: The emphasis has shifted to evaluating your competency via the exam itself. This guarantees that your certification represents your actual knowledge and skills, rather than merely course completion. This could lead to:

Employer confidence: Potential employers can be more confident that certified professionals have the abilities required to flourish in their professions.

Enhanced career prospects: Having a recognized and approved certification might provide you with a competitive advantage in the employment market.

New Certificate Badges: These badges distinguish persons who earned certifications through the traditional path (coursework and exam) from those who received them exclusively through the exam. This could lead to:

Recognition of deeper knowledge: Employers who value a well-rounded learning approach may prefer candidates with badges that match traditional certification paths.

Highlighting dedication: The new badges allow you to demonstrate your initiative in independently gaining the necessary knowledge for the exam.

VMware upgrades its certifications to reflect recent breakthroughs in cloud technologies, like containerization, multi-cloud management, and network virtualization.

Also Read: How to Convert Virtual Machines from VMware to VirtualBox?

Conclusion

Getting a VMware certification shows that you want to stay on top of the latest technologies and learn how to use them. This can give you a big edge in a job market that is very competitive.

The updated VMware certification program makes it easier and faster to show that you know what you’re talking about. As an IT professional, VMware certification can help you whether you’re just starting out or want to get better. You should look into the new program and pick out the certification that fits your needs the best.

FAQs

Here are some frequently asked questions about the updated VMware Certification Program:

What are the biggest changes to the program?

The training course prerequisites have been removed for VCTA, VCP, and VCAP exams and a standardized exam fee structure was introduced.

Where can I find more information about the updates?

You can visit the official VMware Certification website for the latest details.

Do I still need to take any training before taking an exam?

In most cases, no. However, VMware still recommends training to ensure success in the exams.

How much do the exams cost now?

VMware has introduced a flat fee of USD $250 for all VCTA, VCP, and VCAP exams. This simplifies the cost structure and ensures transparency for learners.

What if I already have a VMware certification?

The updates primarily affect new certifications. Existing certifications will remain valid.

How do I register for an exam?

You can register for exams through your VMware Certification account.

Can I reschedule my exam?

Yes, you can either Cancel/Reschedule the exam from your end or contact Pearson VU. Refer to the VMware Certification KB for details.

What are the benefits of becoming VMware certified?

VMware certification validates your skills and knowledge, making you more attractive to employers.

It can help you advance your career and increase your earning potential.

Get Certified. Get Ahead

The Updates to the VMware certification program offers a valuable pathway to career advancement and professional recognition. With the increased flexibility and standardized fees, there’s no better time to invest in your VMware skills and get certified. Visit the VMware Certification homepage to explore the available certifications and chart your course to success.

Also Read: Exploring Proxmox VE: A VMware Alternative

Reference

VMware blog on VMware Certification updates

In the evolving landscape of artificial intelligence (AI), OpenAI stands as a beacon of innovation, continually pushing the boundaries of what’s possible. With each iteration of their Generative Pre-trained Transformer (GPT) series, they redefine the capabilities of natural language processing. Today, we are going to introduce a new era with the introduction of GPT-4o – OpenAI’s latest advancement in AI. 

To improve the naturalness of machine interactions, OpenAI has introduced its new flagship model, GPT-4o, which seamlessly combines text, audio, and visual inputs and outputs. A wider range of input and output modalities are supported by GPT-4o, where the “o” stands for “omni.” OpenAI declared, “It takes any combination of text, audio, and image as input and produces any combination of text, audio, and image outputs.” A remarkable average reaction time of 320 milliseconds is expected from users, with a response time as fast as 232 milliseconds, matching the speed of a human conversation.

Also read: Conversational AI vs Traditional Rule-Based Chatbots: A Comparative Analysis

New Features in GPT-4o

As part of the new model, ChatGPT’s speech mode will get more functionality. The software will have the ability to function as a voice assistant akin to Her, reacting instantly and taking note of your surroundings. The speech mode that is now available is more constrained; it can only hear input and can only react to one suggestion at a time.

Improvements over Previous Models

Significant advancements in natural language processing (NLP) are demonstrated by ChatGPT 4o. The model can now comprehend and produce text with better accuracy and fluency because it was trained on a bigger and more varied dataset. Advantages for Developers: improved creation and documentation of code.

Technical Advancements 

An updated version of the GPT-4 model, which powers OpenAI’s flagship product, ChatGPT, is being introduced as GPT-4o. The new model is substantially faster and has enhanced text, vision, and audio capabilities. All users will be able to use it for free, and those who pay a fee will still be able to utilize it to five times their capacity limits. The text and image capabilities of GPT-4o will be released in ChatGPT, but the rest of its features will be added gradually. Because the model is naturally multimodal, it can produce information and comprehend commands that are given in text, voice, or image formats. The GPT-4o API, which is twice as quick and half as expensive as GPT-4 Turbo, will be available to developers who want to play around with it.

Potential Applications and Benefits

By using a single neural network to process all inputs and outputs, GPT-4o introduces a significant improvement over its predecessors. By using this method, the model can preserve context and important data that were lost in the preceding iterations’ separate model pipeline.

Voice Mode was able to manage audio interactions with latencies of 2.8 seconds for GPT-3.5 and 5.4 seconds for GPT-4 before the GPT-4o launch. Three different models were used in the prior configuration: one for textual answers, one for audio-to-text transcription, and a third for text-to-audio conversion. The loss of subtleties like tone, several voices, and background noise resulted from this segmentation.

GPT-4o is an integrated system that offers significant gains in audio comprehension and vision. More difficult jobs like song harmonization, real-time translation, and even producing outputs with expressive aspects like singing and laughing can be accomplished by it. Its extensive capabilities include the ability to prepare for interviews, translate between languages instantly, and provide customer support solutions.

Performance Benchmarks

While GPT-4o performs at the same level as GPT-4 Turbo in English text and coding tests, it performs noticeably better in non-English languages, indicating that it is a more inclusive and adaptable model. With a high score of 88.7% on the 0-shot COT MMLU (general knowledge questions) and 87.2% on the 5-shot no-CoT MMLU, it establishes a new standard in reasoning.

The model outperforms earlier state-of-the-art models such as Whisper-v3 in audio and translation benchmarks. It performs better in multilingual and vision evaluations, improving OpenAI’s multilingual, audio, and vision capabilities.

Read more: The Introduction of Gemma: Google’s New AI Tool

Addressing Ethical and Safety Concerns

Strong safety features have been designed into GPT-4o by OpenAI, which includes methods for filtering training data and fine-tuning behavior through post-training protections. The model satisfies OpenAI’s voluntary obligations and has been evaluated using a preparedness framework. Assessments in domains such as cybersecurity, persuasion, and model autonomy reveal that GPT-4o falls inside all categories at a risk rating of “Medium.”

To conduct further safety assessments, approximately 70 experts in a variety of fields, including social psychology, bias, fairness, and disinformation, were brought in as external red teams. The goal of this thorough examination is to reduce the hazards brought forth by the new GPT-4o modalities.

Future Implications

GPT-4o’s text and picture features are now available in ChatGPT, with additional features for Plus subscribers as well as a free tier. In the upcoming weeks, ChatGPT Plus will begin alpha testing for a new Voice Mode powered by GPT-4o. For text and vision jobs, developers can use the API to access GPT-4o, which offers double the speed, half the cost, and higher rate limitations than GPT-4 Turbo.

Through the API, OpenAI intends to make GPT-4o’s audio and video capabilities available to a small number of reliable partners; a wider distribution is anticipated soon. With a phased-release approach, the entire range of capabilities will not be made available to the public until after extensive safety and usability testing.

The Potential Impact of GPT-4o on Various Industries

Contradictory sources said that OpenAI was revealing a voice assistant integrated into GPT-4, an AI search engine to compete with Google and Perplexity, or a whole new and enhanced model, GPT-5, before today’s GPT-4o unveiling. Naturally, OpenAI planned this debut to coincide with Google I/O, the tech giant’s premier conference, where we anticipate the introduction of several AI products from the Gemini team.

Also Read: Introducing OpenAI SORA: A text-to-video AI Model

Criticism of GPT-40

The company’s focus has shifted to making those models available to developers through paid APIs and letting those third parties handle the creation after OpenAI came under fire for not making its sophisticated AI models open-source. 

Despite advancements, there are concerns about GPT-4o potentially amplifying biases in its training data. Without careful curation and mitigation strategies, the model could perpetuate or even exacerbate existing societal biases, leading to biased outputs in its language generation.

Conclusion

As we conclude our exploration of GPT-4o, it becomes clear that we’re witnessing a monumental leap forward in AI development. OpenAI’s relentless pursuit of innovation has culminated in a model that surpasses its predecessors in speed, efficiency, and performance. Yet, with great power comes great responsibility. As we harness the potential of GPT-4o and similar advancements, it’s imperative to remain vigilant about the ethical implications, ensuring that AI serves humanity’s best interests. With GPT-4o paving the way, we embark on a journey toward a future where the boundaries between human and machine intelligence blur, promising endless possibilities for innovation and progress.

FAQs

1. What sets GPT-4o apart from previous iterations like GPT-3?

GPT-4o represents a significant advancement in AI technology, boasting enhanced speed, efficiency, and performance compared to its predecessors. Its architecture has been optimized to handle larger datasets and more complex language tasks, resulting in more accurate and contextually relevant outputs. Additionally, GPT-4o incorporates improvements in fine-tuning capabilities, allowing for better customization to specific use cases.

2.  How does GPT-4o address concerns about bias in AI models?

OpenAI has implemented several measures to mitigate bias in GPT-4o. These include extensive data curation and augmentation techniques, as well as fine-tuning strategies to minimize bias amplification during model training. Furthermore, OpenAI continues to prioritize research into fairness, transparency, and accountability in AI systems, striving to create more equitable and unbiased technologies.

3.  What are the practical applications of GPT-4o?

GPT-4o has a wide range of practical applications across various industries and domains. It can be used for natural language understanding tasks such as sentiment analysis, language translation, and question answering. Additionally, GPT-4o’s improved speed and efficiency make it well-suited for real-time applications like chatbots, virtual assistants, and content generation. Its versatility and high performance make GPT-4o a valuable tool for businesses, researchers, and developers seeking to leverage the power of AI in their projects.

DENVER, Colorado, the open source software leader — Red Hat, Inc., the world’s leading provider of open source solutions, on May 7, – RED HAT SUMMIT 2024  announced the launch of Red Hat Enterprise Linux AI (RHEL AI), a foundation model platform that enables users to more seamlessly develop, test and deploy generative AI (GenAI) models.

The headliners are Red Hat Enterprise Linux AI (RHEL AI), a foundation model platform for developing and running open source language models, and InstructLab, a community project to empower domain experts to enhance AI models with their knowledge.

“RHEL AI and the InstructLab project, coupled with Red Hat OpenShift AI at scale, are designed to lower many of the barriers facing GenAI across the hybrid cloud, from limited data science skills to the sheer resources required, while fueling innovation both in enterprise deployments and in upstream communities.”

Ashesh Badani

Senior Vice President and Chief Product Officer, Red Hat

(Resource: Red Hat Delivers Accessible, Open Source Generative AI Innovation with Red Hat Enterprise Linux AI )

How Red Hat stands apart from other companies integrating and offering open source AI

According to Red Hat CEO Matt Hicks, RHEL AI distinguishes itself from the competition in a few key ways.

Primarily, Red Hat is focused on open source and a hybrid approach. “We believe that AI is not really different than applications. That you’re going to need to train them in some places, run them in other places. And we’re neutral to that hardware infrastructure. We want to run anywhere,” said Hicks. 

Additionally, Red Hat has a proven track record of optimizing performance across different hardware stacks. “We have a long history of showing that we can make the most out of the hardware stacks below us. We don’t produce GPUs. I can make Nvidia run as fast as they can. I can make AMD run as fast as they can. I can do the same with Intel and Gaudi,” explained Hicks. 

This ability to make the most of performance across several hardware options while still providing location and hardware optionality is fairly unique in the market.

Finally, Red Hat’s open source approach means customers retain ownership of their IP. “It’s still your IP. We provide that service and subscription business to and you’re not giving up your IP to work with us on that,” said Hicks. 

 (Resource: Red Hat unveils RHEL AI and InstructLab to democratize enterprise AI )

To have a spotlight view discussing Red Hat Enterprise Linux, Red Hat Enterprise Linux AI, Red Hat OpenShift AI, InstructLab, Let’s get started.

Red Hat launches RHEL for AI

The whole solution is packaged as a bootable RHEL image for individual server deployments across the hybrid cloud and is part of OpenShift AI, Red Hat’s hybrid machine learning operations (MLOps) platform for running models and InstructLab at scale through distributed cluster environments. RHEL AI provides a supported, enterprise-ready runtime environment for AI models across AMD, Intel, and Nvidia hardware platforms, Red Hat said…

To lower the entry barriers for AI innovation, enterprises need to be able to expand the roster of who can work on AI initiatives while simultaneously getting these costs under control. With InstructLab alignment tools, Granite models and RHEL AI, Red Hat aims to apply the benefits of true open source projects – freely accessible and reusable, transparent and open to contributions – to GenAI in an effort to remove these obstacles.

Also read: Kairos: Empowering On-Premises Environments with Cloud-Native Meta-Linux Distribution

Building AI in the open with InstructLab

IBM Research generated the Large-scale Alignment for chatBots (LAB) technique, an approach for model alignment that uses taxonomy-guided synthetic data generation and a novel multi-phase tuning framework.

After seeing that the LAB method could help considerably improve model performance, IBM and Red Hat decided to launch InstructLab, an open source community built around the LAB method and the open source Granite models from IBM. The InstructLab project intends to put LLM development into the hands of developers by making, building and contributing to an LLM as simple as contributing to any other open source project.

As part of the InstructLab launch, IBM has also released a family of select Granite English language and code models in the open. These models are released under an Apache license with transparency on the datasets used to train these models. The Granite 7B English language model has been integrated into the InstructLab community, where end users can contribute the skills and knowledge to collectively enhance this model, just as they would when contributing to any other open source project. Similar support for Granite code models within InstructLab will be available soon.

Open source AI innovation on a trusted Linux backbone

RHEL AI builds open approach to AI innovation, integrating an enterprise-ready version of the InstructLab project and the Granite language and code models along with the world’s leading enterprise Linux platform to streamline deployment across a hybrid infrastructure environment. This generates a foundation model platform for bringing open source-licensed GenAI models into the enterprise. RHEL AI includes:

Open source-licensed Granite language and code models that are maintained and indemnified by Red Hat.

A supported, lifecycled distribution of InstructLab that offers a scalable, cost-effective solution for enhancing LLM capabilities and making knowledge and skills contributions accessible to a much comprehensive range of users.

Optimized bootable model runtime instances with Granite models and InstructLab tooling packages as bootable RHEL images via RHEL image mode, containing optimized Pytorch runtime libraries and accelerators for AMD Instinct MI300X, Intel and NVIDIA GPUs and NeMo frameworks.

Red Hat’s ample enterprise support and lifecycle promise that starts with a trusted enterprise product distribution, 24×7 production support and extended lifecycle support.

For instance organizations experiment and tune new AI models on RHEL AI, they have a ready on-ramp for scaling these workflows with Red Hat OpenShift AI, which will include RHEL AI, and where they can leverage OpenShift’s Kubernetes engine to train and serve AI models at scale and OpenShift AI’s integrated MLOps capabilities to manage the model lifecycle.

Also read: Introduction to Proxmox VE 8.1 – Part 1

The cloud is hybrid. So is AI.

This drive of leading  open source technologies continues with Red Hat powering AI/ML strategies across the open hybrid cloud, empowering AI workloads to run where data lives, whether in the datacenter, multiple public clouds or at the edge. More than just the workloads, Red Hat’s vision for AI carries model training and tuning down this same path to better address limitations around data sovereignty, compliance and operational integrity.  The stability delivered by Red Hat’s platforms across these environments, no matter where they run, is crucial in keeping AI innovation flowing.

RHEL AI and the InstructLab community further deliver on this vision, breaking down numerous barriers to experimenting with and building AI models while providing the tools, data and concepts needed to fuel the next wave of intelligent workloads.

Availability

Red Hat Enterprise Linux AI is now available as a developer preview. Building on the GPU infrastructure accessible on IBM Cloud, which is used to train the Granite models and support InstructLab, IBM Cloud will now be adding support for RHEL AI and OpenShift AI. This integration will permit enterprises to deploy generative AI more easily into their mission critical applications.

FAQ’s

What is Red Hat OpenShift AI architecture?

Red Hat OpenShift AI is established on the upstream project Open Data Hub, which is a blueprint for building an AI as a service platform on Red Hat’s Kubernetes-based OpenShift Container Platform. Open Data Hub is a meta-project that incorporates over 20 open source AI/ML projects into a practical solution.

What is the full form of RHEL?

Red Hat Enterprise Linux operating system.

What is OpenShift AI?

Red Hat® OpenShift® AI is a flexible, scalable artificial intelligence (AI) and machine learning (ML) platform that facilitates enterprises to create and deliver AI-enabled applications at scale across hybrid cloud environments.

Is Red Hat and RHEL the same?

RHEL, earlier known as Red Hat Linux Advanced Server, is certified with thousands of vendors and across hundreds of clouds.

Key takeaways:

RHEL AI builds an open approach to AI innovation, incorporating an enterprise-ready version of the InstructLab project and the Granite language and code models along with the world’s leading enterprise Linux platform to streamline deployment across a hybrid infrastructure environment. This forms a foundation model platform for bringing open source-licensed GenAI models into the enterprise.

Therefore, a rapidly growing ecosystem of open model options has spurred further AI innovation and illustrated that there won’t be “one model to rule them all.”

The main hurdle businesses leveraging Proxmox Virtual Environment (VE) for their open-source flexibility and cost-effectiveness have faced is the lack of strong data security options. This often forced them to compromise on business continuity or resort to complex, homegrown solutions. Here’s a great update: Veeam Software, the leader in data protection and ransomware recovery, is puffed off to support Proxmox VE! This exciting development, expected to be widely available in Quarter 3 2024, will significantly enhance data security and disaster recovery capabilities for Proxmox users, especially small and medium-sized businesses (SMBs) and service providers.

If you want to learn more about Proxmox VE, Proxmox VE 8: A Comprehensive Virtualization Course 2024 will help you understand it and completely walk through the product.

Also Read: Proxmox VE 8.2 is Released with VMware ESXi Import Wizard

Why is This Important?

Proxmox VE has gained significant traction in this era due to its ease of use, flexibility, and cost-effectiveness. However, the lack of effective data protection measures has been one of the primary worries for firms utilizing Proxmox.. Veeam’s entry into the Proxmox ecosystem addresses this concern by giving users access to its industry-leading backup and recovery solutions.

What Benefits Does Veeam Bring to Proxmox Users?

By taking advantage of Veeam’s support for Proxmox, users can look forward to enjoying a range of significant benefits:

Comprehensive Data Protection: Veeam’s solutions are purposely directed to provide Proxmox users with a reliable and secure way to back up their virtual machines (VMs), ensuring the safety of critical business data.

Enhanced Disaster Recovery: In the phase of a disaster, Veeam’s recovery tools empower businesses to swiftly restore their VMs and minimize their downtime.

Improved Ransomware Protection: Veeam’s innovative solutions are designed to safeguard organizations’ data from ransomware attacks by delivering immutable backups that ransomware cannot compromise.

Simplified Management: Veeam’s platform provides a centralized management for backups and recoveries across all virtual environments, including Proxmox.

Availability

In the third quarter of 2024, it is planned for the general availability of Veeam’s Support for Proxmox VE. The upcoming VeeamON 2024 conference (June 3-5) will provide more details on the features and functionalities offered.

A Win-Win for Businesses and Veeam

The addition of Proxmox VE support to the Veeam Data Platform represents a mutually beneficial arrangement. Businesses utilizing Proxmox will have access to top-notch data protection solutions, while Veeam will broaden its presence in a booming market segment.

Also Read: Installing Proxmox VE 8.1 on VMware Workstation 17

Conclusion

The upcoming integration of Veeam with Proxmox VE signifies a win-win situation. The Veeam’s support to the Proxmox VE will provide the users with a more enhanced way to proceed in the market. Businesses gain access to industry-leading data protection solutions, ensuring the safety of critical data and minimizing downtime during disruptions. Veeam, on the other hand, expands its reach into an exponentially growing market segment. With VeeamON 2024 just around the corner, we can expect more details on the specific features and functionalities offered.

FAQs

Q: When will Veeam support for Proxmox VE be available?

A: General availability is expected in the third quarter of 2024. More details, including specific features and functionality, are likely to be revealed at VeeamON 2024 (June 3-5).

Q: What are the benefits of Veeam supporting Proxmox VE?

A: Several benefits are anticipated, including:

Comprehensive data protection for your virtual machines (VMs) running on Proxmox VE.

Enhanced disaster recovery capabilities to minimize downtime after disruptions.

Improved ransomware protection with features like immutable backups.

Simplified management through a centralized console for backups across all your virtual environments.

Q: What backup and recovery functionalities can I expect with Veeam for Proxmox?

A: Veeam is likely to offer functionalities like:

Individual VM backups for creating copies for restoration purposes.

Potentially, application-aware backups for specific applications within VMs for data consistency during recovery (feature details to be confirmed).

The ability to recover individual files or folders from backed-up VMs (feature details to be confirmed).

Q: Will Veeam offer disaster recovery features for Proxmox?

A: The expectation is that Veeam will provide functionalities like:

Fast VM recovery to minimize downtime in case of a disaster.

Potentially, replication of backups to a secondary location for disaster recovery purposes (feature details to be confirmed).

Q: How will Veeam’s data protection solutions help against ransomware?

A: Veeam’s solutions are known for offering immutable backups, which are unalterable copies of data. This makes them highly resistant to ransomware attacks, as the malware cannot encrypt these backups.

Q: Where can I learn more about Veeam’s support for Proxmox VE?

A: Here are some resources:

Attend VeeamON 2024 (June 3-5) for the latest announcements.

Visit the Veeam website and blog for updates.

Follow Veeam on social media for news and announcements.

If you want to learn more about Proxmox VE, Proxmox VE 8: A Comprehensive Virtualization Course 2024 will help you understand it and completely walk through the product.

Reference

Vinchin Backup & Recovery, a leading provider of data backup and recovery solutions, unveiled its latest iteration, Vinchin Backup & Recovery 8.0 on May 30, 2024. This upgraded version boasts many new features including Amazon EC2, Microsoft 365, and cloud designed to bolster data security and streamline disaster recovery processes for businesses of all sizes.

Vinchin Backup & Recovery 8.0 delivers regular feature upgrades and expands its capabilities with support for new platforms and innovative disaster recovery features. This includes cloud backup, Continuous Data Protection (CDP), and native integration with popular services like Amazon EC2 and Microsoft 365 (Exchange).

Key New Features of Vinchin Backup & Recovery 8.0

Amazon EC2 Backup

One of the standout features of Vinchin Backup & Recovery 8.0 is the support for Amazon EC2 instance backup. This feature allows businesses to perform agentless backups and recoveries of their Amazon EC2 instances. With data compression, deduplication, and encryption, this feature ensures that your data is not only backed up efficiently but also securely. This capability is crucial for businesses leveraging cloud infrastructure, providing a reliable way to protect and manage their cloud-based resources.

Microsoft 365 Exchange Backup

Vinchin Backup & Recovery 8.0 now supports application-level backup for Microsoft 365 Exchange, including both Exchange Online and Exchange Server. This enhancement offers identity verification and granular recovery options, allowing businesses to restore specific emails or entire mailboxes as needed. This level of detail in backup and recovery operations ensures minimal disruption and quick restoration of critical communication tools.

Continuous Data Protection (CDP)

Continuous Data Protection (CDP) is a game-changer in disaster recovery. Vinchin’s CDP feature provides real-time disaster recovery with near-zero Recovery Point Objectives (RPO). This means that data changes are continuously captured and saved, allowing for automatic failover and ensuring minimal data loss. In the event of a disaster, businesses can quickly switch to a secondary system with the latest data, significantly reducing downtime and data loss.

Cloud Backup

The ability to backup directly to public cloud platforms adds another layer of flexibility and security. Vinchin Backup & Recovery 8.0 supports backup to multiple cloud services including Microsoft Azure, Amazon S3, MinIO, Wasabi, and Ceph. This feature allows businesses to store their backups in geographically diverse locations, enhancing data availability and resilience against local failures or disasters.

Enhanced Features

Vinchin Backup & Recovery 8.0 also introduces several enhanced features aimed at improving overall backup and recovery operations:

Improved OS Recovery with Instant Boot Capabilities: This feature allows for immediate booting of virtual machines from backups, ensuring quick recovery times and minimal disruption.

Optimized File and Database Backup: Enhanced strategies for file and database backups improve the efficiency and reliability of data protection.

Advanced Backup Strategies: Users can now create more sophisticated backup plans tailored to their specific needs, ensuring optimal use of resources and better protection.

Network Reconnection for Backup Tasks: This feature ensures that backup tasks can automatically resume after a network interruption, providing greater reliability and reducing the risk of incomplete backups.

Unified Backup Copy and Cloud Archive Features: These enhancements streamline the backup process, making it easier to manage and store backup copies both on-premises and in the cloud.

Improved Sangfor VM backup: Vinchin is very popular for Sangfor VM protection. To let you protect Sangfor VM in more environments, now you can add Sangfor Cloud Platform to backup the VMs on it.

Also Read: 13 Best VM Backup Solutions in 2023, Features and Pricing

FAQs

What is Continuous Data Protection (CDP) in Vinchin Backup & Recovery 8.0?

CDP in Vinchin Backup & Recovery 8.0 provides real-time disaster recovery by continuously capturing and saving data changes. This ensures near-zero Recovery Point Objectives (RPO), allowing businesses to minimize data loss and quickly switch to a secondary system with the latest data in the event of a disaster.

How does Vinchin Backup & Recovery 8.0 support Amazon EC2 backups?

Vinchin Backup & Recovery 8.0 supports agentless backups and recovery for Amazon EC2 instances. It includes features such as data compression, deduplication, and encryption, ensuring efficient and secure management of cloud-based resources.

Can Vinchin Backup & Recovery 8.0 backup Microsoft 365 Exchange data?

Yes, Vinchin Backup & Recovery 8.0 supports application-level backup for Microsoft 365 Exchange, including Exchange Online and Exchange Server. It provides identity verification and granular recovery options, allowing for the restoration of specific emails or entire mailboxes.

What cloud platforms are supported for direct backup in Vinchin Backup & Recovery 8.0?

Vinchin Backup & Recovery 8.0 supports direct backup to multiple public cloud platforms, including Microsoft Azure, Amazon S3, MinIO, Wasabi, and Ceph. This allows businesses to store backups in geographically diverse locations, enhancing data availability and resilience.

Conclusion

Vinchin Backup & Recovery 8.0 is a comprehensive and robust solution designed to meet the evolving needs of modern businesses. The new features and enhancements provide a significant boost in data protection capabilities, ensuring that businesses can safeguard their critical data and recover swiftly from any disruptions. By supporting a wide range of environments and offering advanced backup and recovery options, Vinchin continues to lead the way in data protection solutions.

Stay ahead in data protection with Vinchin Backup & Recovery 8.0. Explore the new features and see how they can benefit your business. For more detailed information, visit the official release page. Protect your data, ensure business continuity, and enhance your disaster recovery strategy with Vinchin’s latest offering.

Stay tuned to Techwrix for more updates and insights on the latest tech innovations and solutions.

Also Read: How to Back up VMware VMs with NAKIVO Backup & Replication?

The benefits, risks, and considerations associated with using open-source LLMs, as well as the comparison with proprietary models.

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Large language models (LLMs) powered by artificial intelligence are gaining immense popularity, with over 325,000 models available on Hugging Face. As more models emerge, a key question is whether to use proprietary or open-source LLMs.

What are LLMs and How Do They Differ?LLMs leverage deep learning and massive datasets to generate human-like textProprietary LLMs are owned and controlled by a companyOpen-source LLMs are freely accessible for anyone to use and modifyProprietary models currently tend to be much larger in terms of parametersHowever, size isn’t everything – smaller open-source models are rapidly catching upCommunity contributions empower the evolution of open-source LLMs

How Do (LLM) Large Language Models Work? Explained

A large language model (LLM) is an AI system trained on extensive text data, designed to produce human-like and intelligent responses.

Benefits of Open Source LLMsTransparency – Better visibility into model architecture, training data, output generationCustomization through fine-tuning custom datasets for specific use casesCommunity contributions across diverse perspectives enable experimentationUse Cases

Open-source LLMs are being deployed across industries:

HealthcareDiagnostic assistanceTreatment optimizationFinanceApplications like FinGPT for financial analysisScienceModels like NASA’s trained on geospatial dataLeading Models on Hugging Face

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Top LLMs on Hugging Face

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Downside of Open-source LLMs

Despite advances, LLMs have concerning have 3 major limitations:

Inaccuracy – Hallucinations from inaccurate/incomplete training dataSecurity – Potential exposure of private data in outputsBias – Embedding biases that skew outputs

Mitigating these risks in early-stage LLMs remains vital.

The Bottom Line

Open-source big language models make AI more available to everyone. This widens who can use them. But risks are still there. Even so, putting information out in the open and letting users adjust models to their needs gives power to people across fields.

In-memory caching and in-memory data storage are both techniques used to improve the performance of applications by storing frequently accessed data in memory. However, they differ in their approach and purpose.

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In-memory caching and in-memory data storage are both techniques used to improve the performance of applications by storing frequently accessed data in memory. However, they differ in their approach and purpose.

What is In-Memory Caching?

In-memory caching is a method where data is temporarily stored in the system’s primary memory (RAM). This approach significantly reduces data access time compared to traditional disk-based storage, leading to faster retrieval and improved application performance.

Key Features:Speed: Caching provides near-instant data access, crucial for high-performance applications.Temporary Storage: Data stored in a cache is ephemeral, and primarily used for frequently accessed data.Reduced Load on Primary Database: By storing frequently requested data, it reduces the number of queries to the main database.Common Use Cases:Web Application Performance: Improving response times in web services and applications.Real-Time Data Processing: Essential in scenarios like stock trading platforms where speed is critical.

In-Memory Caching: This is a method to store data temporarily in the system’s main memory (RAM) for rapid access. It’s primarily used to speed up data retrieval by avoiding the need to fetch data from slower storage systems like databases or disk files. Examples include Redis and Memcached when used as caches.

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What is an In-Memory Data Store?

An In-Memory Data Store is a type of database management system that utilizes main memory for data storage, offering high throughput and low-latency data access.

Key Features:Persistence: Unlike caching, in-memory data stores can persist data, making them suitable as primary data storage solutions.High Throughput and Low Latency: Ideal for applications requiring rapid data processing and manipulation.Scalability: Easily scalable to manage large volumes of data.Common Use Cases:Real-Time Analytics: Used in scenarios requiring quick analysis of large datasets, like fraud detection systems.Session Storage: Maintaining user session information in web applications.

In-Memory Data Store: This refers to a data management system where the entire dataset is held in the main memory. It’s not just a cache but a primary data store, ensuring faster data processing and real-time access. Redis, when used as a primary database, is an example.

How Do (LLM) Large Language Models Work? Explained

A large language model (LLM) is an AI system trained on extensive text data, designed to produce human-like and intelligent responses.

Comparing In-Memory Caching and In-Memory Data Store

Aspect
In-Memory Caching
In-Memory Data Store

Purpose
Temporary data storage for quick access
Primary data storage for high-speed data processing

Data Persistence
Typically non-persistent
Persistent

Use Case
Reducing database load, improving response time
Real-time analytics, session storage, etc.

Scalability
Limited by memory size, often used alongside other storage solutions
Highly scalable, can handle large volumes of data

Advantages and LimitationsIn-Memory Caching

Advantages:

Reduces database load.Improves application response time.

Limitations:

Data volatility.Limited storage capacity.In-Memory Data Store

Advantages:

High-speed data access and processing.Data persistence.

Limitations:

Higher cost due to large RAM requirements.Complexity in data management and scaling.

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Choosing the Right Approach

The choice between in-memory caching and data store depends on specific application needs:

Performance vs. Persistence: Choose caching for improved performance in data retrieval and in-memory data stores for persistent, high-speed data processing.Cost vs. Complexity: In-memory caching is less costly but might not offer the complexity required for certain applications.Summary

To summarize, some key differences between in-memory caching and in-memory data stores:

Caches hold a subset of hot data, and in-memory stores hold the full dataset.Caches load data on demand, and in-memory stores load data upfront.Caches synchronize with the underlying database asynchronously, and in-memory stores sync writes directly.Caches can expire and evict data, leading to stale data. In-memory stores always have accurate data.Caches are suitable for performance optimization. In-memory stores allow new applications with real-time analytics.Caches lose data when restarted and have to repopulate. In-memory stores maintain data in memory persistently.Caches require less memory while in-memory stores require sufficient memory for the full dataset.

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Cloudflare is moving from NGINX to Pingora, it solves the primary reverse proxy and caching needs and even for web server’s request handling.

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NGINX as a reverse proxy has long been a popular choice for its efficiency and reliability. However, Cloudflare announced their decision to move away from NGINX to their homegrown open-source solution for reverse proxy, Pingora.

What is Reverse Proxy?

A reverse proxy sits in front of the origin servers and acts as an intermediary, receiving requests, processing them as needed, and then forwarding them to the appropriate server. It helps improve performance, security, and scalability for websites and web applications.

Imagine you want to visit a popular website like Wikipedia. Instead of going directly to Wikipedia’s servers, your request first goes to a reverse proxy server.

The reverse proxy acts like a middleman. It receives your request and forwards it to one of Wikipedia’s actual servers (the origin servers) that can handle the request.

When the Wikipedia server responds with the requested content (like a web page), the response goes back to the reverse proxy first. The reverse proxy can then do some additional processing on the content before sending it back to you.

What is the difference between Forward Proxy vs Reverse Proxy?

Understand, The role that proxies play in web architecture and consider using them to improve the performance, security, and scalability of your site.

Reverse Proxy is used for:Caching: The reverse proxy stores frequently requested content in its memory. So if someone else requests the same Wikipedia page, the reverse proxy can quickly serve it from the cache instead of going to the origin server again.Load balancing: If there are multiple Wikipedia servers, the reverse proxy can distribute incoming requests across them to balance the load and prevent any single server from getting overwhelmed.Security: The reverse proxy can protect the origin servers by filtering out malicious requests or attacks before they reach the servers.Compression: The reverse proxy can compress the content to make it smaller, reducing the amount of data that needs to be transferred to you.SSL/TLS termination: The reverse proxy can handle the encryption/decryption of traffic, offloading this work from the origin servers.Why Does Cloudflare Have a Problem with NGINX?

While NGINX has been a reliable workhorse for many years, Cloudflare encountered several architectural limitations that prompted it to seek an alternative solution. One of the main issues was NGINX’s process-based model. Each request was handled by a separate process, which led to inefficiencies in resource utilization and memory fragmentation.

Another challenge Cloudflare faced was the difficulty in sharing connection pools among worker processes in NGINX. Since each process had its isolated connection pool, Cloudflare found itself executing redundant SSL/TLS handshakes and connection establishments, leading to performance overhead.

Furthermore, Cloudflare struggled with adding new features and customizations to NGINX due to its codebase being written in C, a language known for its memory safety issues.

In-Memory Caching vs. In-Memory Data Store

In-memory caching and in-memory data storage are both techniques used to improve the performance of applications by storing frequently accessed data in memory. However, they differ in their approach and purpose.

How Cloudflare Built Its Reverse Proxy “Pingora” from Scratch?

Being Faced with these limitations, Cloudflare considered several options, including forking NGINX, migrating to a third-party proxy like Envoy, or building their solution from scratch. Ultimately, they chose the latter approach, aiming to create a more scalable and customizable proxy that could better meet their unique needs.

Feature
NGINX
Pingora

Architecture
Process-based
Multi-threaded

Connection Pooling
Isolated per process
Shared across threads

Customization
Limited by configuration
Extensive customization via APIs and callbacks

Language
C
Rust

Memory Safety
Prone to memory safety issues
Memory safety guarantees with Rust

To address the memory safety concerns, Cloudflare opted to use Rust, a systems programming language known for its memory safety guarantees and performance. Additionally, Pingora was designed with a multi-threaded architecture, offering advantages over NGINX’s multi-process model.

With the help of multi-threading, Pingora can efficiently share resources, such as connection pools, across multiple threads. This approach eliminates the need for redundant SSL/TLS handshakes and connection establishments, improving overall performance and reducing latency.

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The Advantages of Pingora

One of the main advantages of Pingora is its shared connection pooling capability. By allowing multiple threads to access a global connection pool, Pingora minimizes the need for establishing new connections to the backend servers, resulting in significant performance gains and reduced overhead.

Cloudflare also highlighted Pingora’s multi-threading architecture as a major benefit. Unlike NGINX’s process-based model, which can lead to resource contention and inefficiencies, Pingora’s threads can efficiently share resources and leverage techniques like work stealing to balance workloads dynamically.

Pingora: A Rust Framework for Network Services

Interestingly, Cloudflare has positioned Pingora as more than just a reverse proxy. They have open-sourced Pingora as a Rust framework for building programmable network services. This framework provides libraries and APIs for handling protocols like HTTP/1, HTTP/2, and gRPC, as well as load balancing, failover strategies, and security features like OpenSSL and BoringSSL integration.

The selling point of Pingora is its extensive customization capabilities. Users can leverage Pingora’s filters and callbacks to tailor how requests are processed, transformed, and forwarded. This level of customization is particularly appealing for services that require extensive modifications or unique features not typically found in traditional proxies.

The Impact on Service Meshes

As Pingora gains traction, it’s natural to wonder about its potential impact on existing service mesh solutions like Linkerd, Istio, and Envoy. These service meshes have established themselves as crucial components in modern microservices architectures, providing features like traffic management, observability, and security.

While Pingora may not directly compete with these service meshes in terms of their comprehensive feature sets, it could potentially disrupt the reverse proxy landscape. Service mesh adopters might consider leveraging Pingora’s customizable architecture and Rust-based foundation for building their custom proxies or integrating them into their existing service mesh solutions.

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The Possibility of a “Vanilla” Pingora Proxy

Given Pingora’s extensive customization capabilities, some speculate that a “vanilla” version of Pingora, pre-configured with common proxy settings, might emerge in the future. This could potentially appeal to users who desire an out-of-the-box solution while still benefiting from Pingora’s performance and security advantages.

Self-host the open-source, privacy-focused note-taking app Memos using Docker with a MySQL database and integrate with S3 or Cloudflare R2 object storage.

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What is Memos?

Memos is an open-source, privacy-first, and lightweight note-taking application service that allows you to easily capture and share your thoughts.

Memos features:Open-source and free foreverSelf-hosting with Docker in secondsPure text with Markdown supportCustomize and share notes effortlesslyRESTful API for third-party integrationSelf-Hosting Memos with Docker and MySQL Database

You can self-host Memos quickly using Docker Compose with a MySQL database.

Prerequisites: Docker and Docker Compose installed

You have two options to choose MySQL or MariaDB as a Database both are stable versions and MariaDB consumes less memory than MySQL.

Memos with MySQL 8.0version: “3.0”

services:

mysql:
image: mysql:8.0
environment:
TZ: Asia/Kolkata
MYSQL_ROOT_PASSWORD: memos
MYSQL_DATABASE: memos-db
MYSQL_USER: memos
MYSQL_PASSWORD: memos
volumes:
– mysql_data:/var/lib/mysql

memos:
image: neosmemo/memos:stable
container_name: memos
environment:
MEMOS_DRIVER: mysql
MEMOS_DSN: memos:memos@tcp(mysql:3306)/memos-db
depends_on:
– mysql
volumes:
– ~/.memos/:/var/opt/memos
ports:
– “5230:5230”

volumes:
mysql_data:

Memos with MySQL Database Docker Compose

ORMemos with MariaDB 11.0version: “3.0”
services:
mariadb:
image: mariadb:11.0
environment:
TZ: Asia/Kolkata
MYSQL_ROOT_PASSWORD: memos
MYSQL_DATABASE: memos-db
MYSQL_USER: memos
MYSQL_PASSWORD: memos
volumes:
– mariadb_data:/var/lib/mysql

memos:
image: neosmemo/memos:stable
container_name: memos
environment:
MEMOS_DRIVER: mysql
MEMOS_DSN: memos:memos@tcp(mariadb:3306)/memos-db
depends_on:
– mariadb
volumes:
– ~/.memos/:/var/opt/memos
ports:
– “5230:5230”

volumes:
mariadb_data:

Memos with MariaDB Database Docker Compose

Create a new file named docker-compose.yml and copy the above content.This sets up a MariaDB 11.0 database service and the Memos app linked to it.Run docker-compose up -d to start the services in detached mode.Memos will be available at http://localhost:5230.The configurations are:mysql service runs MySQL 8.0 with a database named memos-db.memos service runs the latest Memos images, and links to the mysql/mariadb service.MEMOS_DRIVER=mysql tells Memos to use the MySQL database driver.MEMOS_DSN contains the database connection details.The ~/.memos the directory is mounted for data persistence.

You can customize the MySQL password, database name, and other settings by updating the environment variables.

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Configuring S3 Compatible Storage

Memos support integrating with S3-compatible object storage like Amazon S3, Cloudflare R2, DigitalOcean Spaces, etc

To use AWS S3/ Cloudflare’s R2 as object storageSettings > StorageCreate a S3/Cloudflare R2 bucketGet the API token with object read/write permissionsIn Memos Admin Settings > Storage, create a new storageEnter details like Name, Endpoint, Region, Access Key, Secret Key, Bucket name and Public URL (For Cloudflare R2 set Region = auto)Save and select this storageFor Cloudflare R2 set Region = auto

With this setup, you can self-host the privacy-focused Memos note app using Docker Compose with a MySQL database, while integrating scalable S3 or R2 storage for persisting data.

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The process of creating a web application is quite time-consuming and multi-stage. That is why a whole team of specialists with relevant skills is working on it. Each of them performs an important mission and has their own responsibilities.

However, among all these specialists, it is worth highlighting a senior backend developer – a specialist who takes care of creating a functional and reliable technical basis. In this article, we offer to learn in more detail what exactly such a specialist does and to understand their role in the process.

The Foundation of a New Web Application

To understand how valuable these specialists are in the development process, it is enough to analyze the specifics of their activity. Backend developers are those people who deal with all components of the internal part of the software solution. That is, their responsibilities include maintaining the operation of data processing mechanisms, creating a basis for security and signaling (interface interaction), and running various functions on the server side.

All this cannot be seen by the users, but they can evaluate the responsiveness of the buttons, the specifics of performing certain actions in the web application, and other points related to the internal functionality. It is worth noting that the responsibilities of backend developers also include processing and managing large amounts of data. For this, experts create effective solutions for searching and storing data.

Cooperation and Integration

In the process of work, backend specialists actively interact with the teams that develop the interface. They create certain technological solutions that serve as points for data exchange between parties. This is the key to the harmonious development of a software product with minimal risks of errors and bugs. In addition, such points are the basis for real-time updates and smooth interaction.

Protection of Databases

The responsibility of establishing a system for reliable protection of confidential information of users and owners is also assumed by the backend developer. They implement special authorization and authentication mechanisms that allow you to control visits to the web application and protect it from hacking.

Access levels are also implemented. For example, only authorized users can interact with certain components. Another important step towards this is the implementation of special encryption protocols. They guarantee additional protection against unauthorized use of data.

Traffic Scaling and Handling

During the use of each software product, certain waves of activity are observed. There may be peak loads at some hours, and no traffic at all at others. Such fluctuations are harmful to the web application, so there is a need for load regulation. This is also the responsibility of the backend developer.

For this, the specialist usually uses different methods of distributing the load between several servers. In particular, thanks to the introduction of caching mechanisms, as well as balancing technology. In this way, the risks of failures in the web application are significantly minimized, which allows you to avoid large losses from minor failures.

Conclusions

The backend developer is one of the key figures in the process of creating a web application. This is due to the specifics of the work of such a specialist. They handle all internal processes: code creation, security, and functionality setup, creating channels for interaction with frontend developers, as well as scaling and data processing. This means that their skills and knowledge contribute not only to the functionality but also to the efficiency and security of the software solution.