The Apache Cassandra team previously said that support for versions 3.0 and 3.11 would end at the end of 2023, following the release of version 5.0, but it has now been extended to 2024.

Microsoft is extending support for Apache Cassandra 3.11 in addition to developing turnkey in-place upgrades to assist administrators in transitioning to the upgrade, with the goal of encouraging users to move to Cassandra versions 4.0 or 4.1.

Apache Cassandra is a free and open-source distributed NOSQL database designed to handle large amounts of data.

Apache Cassandra is a cross-platform and one of the best NOSQL databases for big data. It is a highly scalable, high-performance, and highly available large database that allows for the management of large amounts of data distributed across numerous servers.

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A few days ago, according to foreign media The Information, Google seems to be “unable to hold back” and is currently “racing against time” preparing to release this Gemini AI this fall to catch up with competitors such as OpenAI’s GPT-4.

▲ In the tide of AI, foreign media believe that Google is facing the risk of “falling behind”

According to foreign media, Google’s Gemini AI combines GPT-4-like text generation capabilities and supports the feature of “creating images based on text descriptions”, so the AI tool can realize “chat dialogue”, “analyze chart data”, “create image “, “Even control software with natural language commands” and other functions.

It is reported that Google hopes that Gemini AI can provide support for consumer AI products such as Google’s Bard chatbot. Such key integration behaviours will help Google Cloud better compete head-on with competing products such as Microsoft Azure.

Foreign media also said that the timing of Google’s release of Gemini AI this fall is crucial, and Google is currently facing the danger of “falling behind” at the AI ​​level.

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In 2021, Microsoft introduced Jenny Multilingual, a female voice that can naturally speak many different languages. Today, Microsoft announced an upgrade to Azure AI text-to-speech. Not only does it add more language support, it also adds a male voice — Ryan Multilingual.

In a blog post, Microsoft said that this addition not only provides users with more options, but also enables them to create inclusive and diverse user experiences. By introducing male voices, Azure Text-to-Speech ensures that the technology adapts to different scenarios and user preferences, enabling developers to design solutions that resonate with a wider range of end users. Additionally, both new voices feature automatic language prediction that automatically recognizes the input language based on the input text and adjusts the speech output accordingly, eliminating the need for manual tagging. In addition.

This update expands the number of supported languages from 14 to 41, as listed below:

ar-EG

ar-SA

ca-ES

cs-CZ

da-DK

de-AT

de-CH

de-DE

en-AU

en-CA

en-GB

en-HK

en-HK

en-IN

en-US

en-US

es-MX

(b) the organization of the work of the Committee on Conventions and Recommendations (CRIC)

fi-FI

fi-FI

fr-CH

fr-FR

hi-IN

hu-HU

hu-HU

it-IT

ja-JP

ko-KR

nb-NO

nl-BE

nl-NL

pl-PL

pt-BR

pt-PT

ru-RU

sv-SE

th-TH

tr-TR

zh-CN

zh-HK

zh-TW

In addition it is noted that Microsoft also said it has developed new systems that automatically change the pronunciation, intonation and phonology of its AI voices to best match the specific language of the input. This breakthrough enables users to easily switch between languages during text input, creating a more immersive and authentic multilingual experience, Microsoft said.

The new male voices, language support and adaptive system are currently in public preview in Azure’s Eastern U.S., Western Europe and Southeast Asia regions.

The post Microsoft Azure AI text-to-speech service upgrade, male voice and more language support appeared first on TechGoing.

Microsoft said the Azure OpenAI service communicates securely through Transport Layer Security (TLS), and Azure Government connects directly to the commercial Microsoft Azure network, never interfacing with the public Internet or Microsoft corporate network.

In addition, Microsoft says Azure Government customers’ data is never used to train OpenAI models. similar measures are in place on ChatGPT, but are not the default setting.

Azure OpenAI Services enables government customers to use GPT-4, GPT-3, and embedding through the Azure OpenAI Services REST API, which you can use to accelerate content generation, simplify content summarization, optimize semantic search, and streamline code generation. Microsoft has extended each of these as described below:

• Accelerate content generation: Automatically generate responses based on task or project queries to help reduce the time and effort required for research and analysis, allowing teams to focus on higher-level decision-making and strategic tasks.
• Streamlined Content Summary: Generate log summaries and quickly analyze articles, analysts and field reports.
• Optimize semantic search: Enable enhanced information discovery and knowledge mining.
• Simplify code generation: Build custom applications using natural language to query proprietary data models and quickly generate code documentation.

“One of the most effective ways to generate reliable answers is to prompt models to derive responses from the underlying data,” said Bill Chappell, chief technology officer for strategic tasks and technologies at Microsoft. “If your use cases rely on up-to-date, reliable information rather than purely creative scenarios, we strongly recommend providing underlying data based on trusted internal data sources.”

“In general, the closer your source material is to the final form of the answer you want, the less work the model needs to do, which means less opportunity for error.”

Microsoft Azure OpenAI services require registration and are available only to approved customers and partners. Microsoft has released Azure Government OpenAI Access QuickStart to help governments use it (the relevant address).

The post Microsoft Azure OpenAI government customers to gain secure access to generative AI APIs appeared first on TechGoing.

Azure DevOps provides an integrated set of services and tools for managing software projects, from planning and development to testing and deployment. Eric Mattingly explained that Azure DevOps engineers sometimes take snapshots of production databases in order to investigate reported issues or test performance improvements. They rely on a background system that runs daily and deletes old snapshots after a certain amount of time. Recently, Azure DevOps engineers performed a code upgrade, replacing the deprecated Microsoft.Azure.Managment.* packages with supported Azure.ResourceManager.* NuGet packages. This resulted in a large pull request that replaced the API calls in the old package and the new package.

The typo is in this pull request, which replaces the call to delete the snapshot database with a call to delete the Azure SQL Server hosting the database. Azure DevOps has tests designed to catch such issues, but Eric Mattingly said that because the faulty code only runs under certain conditions, existing tests don’t cover it.

A few days later, the software changes were deployed to the customer’s environment at the South Brazil scale unit (a cluster of role-specific servers). The environment had an old snapshot database that triggered the bug, causing a background task to delete “the entire Azure SQL Server and all seventeen production databases”.

All data has been recovered, but it took more than ten hours. There are several reasons for this, Eric Mattingly said. One of them is that since the customer cannot recover the Azure SQL Server themselves, it must be handled by the on-call Azure engineer, which takes about an hour. Another reason is that databases have different backup configurations: some configured as region-redundant backups, some as newer geo-region-redundant backups, and resolving this mismatch adds significant recovery times.

In order to prevent the problem from happening again, Eric Mattingly said that Microsoft has taken various fixes and reconfiguration measures, and once again apologized to all customers affected by this outage.

The post A code spelling error causes Microsoft Azure DevOps service to be down for ten hours in Brazil appeared first on TechGoing.

In a blog post, Microsoft stated:

"GitHub Advanced Security for Azure DevOps brings the same developer security features to Azure DevOps as GitHub Advanced Security, integrated directly into Azure Repos and Azure Pipelines This includes the same secret scanning, dependency scanning, and CodeQL code scanning capabilities available in GitHub Enterprise."

Confidential scanning is helpful for developers who are afraid of exposing confidential credentials – “50% of all security breaches are due to credential exposure.

Using the features in GitHub Advanced Security for Azure DevOps can find any previously released secrets and also stop them before they are compromised.

"Depending on how widely the secrets are used, this can take days of effort and stress - if you miss rotating secrets in only one of the places where they are used, it can lead to a live site outage. On the other hand, if you stop the confidential from being exposed when you push, it's easier to clean up your commit and re-push it before it persists in Azure Repos."

The new GitHub service can also discover any open-source package vulnerabilities with the Dependency Scan feature. In addition, it uses the CodeQL static analysis engine to let developers find hundreds of security issues from a variety of code languages.

GitHub Advanced Security for Azure DevOps billing is handled through Azure and costs $49 per month per active committer (currently about RMB 346), see more at this link.

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Microsoft says the new system is designed to help application developers connect to cloud services in a self-service manner so they can spend more time writing code.

Developers can access their environments quickly and easily with on-demand or automated deployments,” Microsoft said. If developers have ideas on how to improve their environment, they can edit and submit their own templates via pull requests to the Infrastructure as Code Template Repository — helping to promote best practices and InnerSource (internal open source) among their teams.”

Microsoft says this approach to cloud application development also helps reduce a significant amount of redundant work, due to the fact that administrators can set up Azure deployment environments using their own pre-approved templates, which will also help collaboration among application developers.

In addition, Microsoft has added support for Terraform infrastructure-as-code files in Azure deployment environments in Early Access, which application developers can now sign up for.

The service already supports Azure Resource Manager templates; adding Terraform support means customers using Terraform will be able to import their existing templates directly into their Azure deployment environments,” Microsoft said. “

Microsoft added that it plans to add support for other infrastructure-as-code formats, such as Pulumi and Ansible, but did not disclose exactly when this support will be added to Azure deployment environments.

The post Microsoft officially releases Azure Deployment Environment, free of charge appeared first on TechGoing.

Microsoft is now shipping the ND H100 v5 VM, Azure’s more powerful and highly scalable family of AI virtual machines to date. The VMs support on-demand configurations of up to 8 to thousands of NVIDIA H100 GPUs interconnected over Quantum-2 InfiniBand networks, enabling the significantly higher performance of AI models. Compared to the previous generation of ND A100 v4 VMs, this virtual machine release includes the following innovations:

• 8 NVIDIA H100 Tensor Core GPUs interconnected via next-generation NV Switch and NV Link 4.0.
• Each GPU is equipped with 400 Gb/s NVIDIA Quantum-2 CX7 InfiniBand and 3.2Tb/s performance per VM in a non-blocking fat-tree network.
• The eight local GPUs in each VM are interconnected with each other via NV Switch and NV Link 4.0 with 3.8Tb/s of pairwise bandwidth.
• 4th generation Intel Xeon Scalable processors.
• PCIE 5th generation host to GPU interconnect with 64Gb/s per GPU bandwidth.
• 16-channel 4800 MHz DDR5 memory.

NVIDIA Quantum-2 uses the 7th generation NVIDIA InfiniBand architecture to provide AI developers and scientific researchers with superb network performance and rich features to help them solve challenging problems, remote direct memory access (RDMA) and ultra-fast speeds of up to 400 Gb/s to power advanced supercomputing data centers.

Microsoft says that large-scale AI is built into Azure’s DNA. Initial investments in large-scale language modelling research, such as Turing, and milestones such as building the first AI supercomputers in the cloud, have prepared the ground for the introduction of generative AI. Azure OpenAI services enable customers to leverage the power of large-scale generative AI models. “Scale” has always been one of the goals of Azure’s optimized AI infrastructure. Now, Microsoft is bringing supercomputing capabilities to startups and enterprises of all sizes without significant physical hardware or software investment.

Now, with the preview release of ND H100 v5, it will become a standard service in the Azure portfolio.

The post Microsoft Azure ND H100 v5 VM Preview Released: 8 to Thousands of NVIDIA H100 Tensor Core GPUs appeared first on TechGoing.

Since April, Microsoft has been testing the ARM architecture to support its Azure VM virtual machines. Microsoft is offering a “preview” version of ARM support on Azure VMs. Microsoft claims that “hundreds” of customers have been testing ARM-based Azure VM virtual machines.

The new Azure ARM-based VMs are available in the following configurations.

Dpsv5 Series with up to 64 vCPUs and 4 GiB of memory, up to 208 GiBs per vCPU

Dplsv5 Series with up to 64 vCPUs and 2 GiB of memory, up to 128 GiBs per vCPU

Epsv5 Series, up to 32 vCPUs and 8 GiB of memory, up to 208 GiBs per vCPU

These VMs will support up to 40Gbps network bandwidth; standard SSDs, standard HDDs, advanced SSDs and hyperdisk storage can be attached to the VMs. Azure Monitor and Azure Backup will help monitor data health and VM performance parameters.

The aforementioned ARM-powered Azure VMs will now move into “universal availability”. Going forward, Azure VMs with Ampere Altra ARM processors will be available in “10 Azure regions and multiple availability zones worldwide”.

These will be available to users in the US (US West 2, US Midwest, US Central, US East, US East 2); Europe (Western Europe, Northern Europe); Asia (East Asia, Southeast Asia) and Australia (Eastern Australia). Microsoft has pledged to add several more regions after Sept. 1.

Microsoft confirmed that during the trial phase, developers and Web administrators are using VM for Web and application servers, open source databases, microservices, Java and .NET applications, games and media servers. In addition to these platforms, ARM-based VMs can also be included in Kubernetes clusters, and Azure Kubernetes Services (AKS) can and will accept new VMs.

Azure VMs powered by ARM CPUs can reliably run Windows 11 ARM Professional and Enterprise editions. In addition, these processors support a wide range of popular Linux distributions. In addition to Ubuntu, Red Hat Enterprise Linux, SUSE Enterprise Linux, CentOS and Debian, Microsoft will gradually add support for Alma Linux and Rocky Linux.

The post Microsoft Azure VMs fully enables ARM CPU support: running Windows 11 appeared first on TechGoing.