/ By InfoWorld Cloud Computing / 0 Comments

Kubernetes plays an important role at Microsoft. The container management system is a foundational piece of the company’s many clouds, from Microsoft 365 and Xbox, to Azure, to partners like OpenAI that use Microsoft’s Kubernetes to host their own services.

As a result, Microsoft has invented many of its own Kubernetes management tools. These include Kaito for deploying AI inferencing workloads and Fleet for large-scale management of Kubernetes clusters. All of Microsoft’s various tools sit underneath its two managed Kubernetes services, Azure Kubernetes Service and Azure Container Service, allowing you to deploy and orchestrate your container-based applications without needing to build the necessary management framework. It all comes for free, with APIs, portals, and command line interfaces.

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/ By InfoWorld Cloud Computing / 0 Comments
/ By InfoWorld Cloud Computing / 0 Comments
/ By InfoWorld Cloud Computing / 0 Comments
/ By Martin_Heller@idg.contractors / 0 Comments

Vertex AI Studio is an online environment for building AI apps, featuring Gemini, Google’s own multimodal generative AI model that can work with text, code, audio, images, and video. In addition to Gemini, Vertex AI provides access to more than 40 proprietary models and more than 60 open source models in its Model Garden, for example the proprietary PaLM 2, Imagen, and Codey models from Google Research, open source models like Llama 2 from Meta, and Claude 2 and Claude 3 from Anthropic. Vertex AI also offers pre-trained APIs for speech, natural language, translation, and vision.

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Years ago, I found myself sitting in a conference room dealing with a question that I’ve gotten thousands of times in my career: Was the technology I was representing, which I created as the CTO of an enterprise technology company (I’m being purposely vague), the right fit for a specific customer problem that I had just learned about in that conference room?

This would have been an easy sale and one that we needed to make our quarter. All I needed to do was agree with the customer, who was already convinced that my technology was a “perfect fit.” Instead, I explained that a competitor’s technology was likely a better fit and had the features and functions needed for this problem. The salesperson shot daggers at me from across the table. Commission gone.

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/ By Martin_Heller@idg.contractors / 0 Comments

Vertex AI Studio is an online environment for building AI apps, featuring Gemini, Google’s own multimodal generative AI model that can work with text, code, audio, images, and video. In addition to Gemini, Vertex AI provides access to more than 40 proprietary models and more than 60 open source models in its Model Garden, for example the proprietary PaLM 2, Imagen, and Codey models from Google Research, open source models like Llama 2 from Meta, and Claude 2 and Claude 3 from Anthropic. Vertex AI also offers pre-trained APIs for speech, natural language, translation, and vision.

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Starting with Redis 7.4, all future versions of Redis software will be dual-licensed under the Redis Source Available License (RSAL 2) and the Server Side Public License (SSLPv1), Redis announced. The popular NoSQL database will no longer be distributed under the three-clause Berkeley Software Distribution (BSD) license.

New source-available licenses will allow Redis the company to provide permissive use of its source code, the company said on March 20. Source code will continue to be freely available to developers, customers, and partners through Redis Community Edition.

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/ By InfoWorld Security / 0 Comments

The key benefits of platform engineering are increased developer productivity, better quality of software, reduced lead time for deployment, and more stable applications, according to Puppet by Perforce’s 2024 State of Devops Report: The Evolution of Platform Engineering.

The report is based on a survey of 474 participants who work with a platform engineering team at their organizations. The survey was conducted in the summer of 2023.

Other benefits cited include cost savings, reduced time for product development, reduced errors, and reduced risk of security breaches. “Security has never just been IT’s job,” said Kapil Tandon, Puppet by Perforce vice president of product management, in the executive summary. “With secure tools built into most platforms, platform engineering is empowering more people than ever to take responsibility for security.”

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The benefits of developing software in the cloud include increased flexibility and reliability, greater efficiency, and reduced costs. But cloud-based development also presents a host of challenges. Knowing what to watch out for is the first step to protecting your applications and development efforts. Here, are 10 pitfalls to consider before developing, testing, or deploying applications in the cloud.

10 reasons to think twice before developing in the cloud

  1. Performance and latency issues
  2. Cybersecurity and data protection threats
  3. Vendor lock-in
  4. Runaway costs
  5. Regulatory compliance requirements
  6. Compatibility and integration issues
  7. Scalability demands
  8. Distributed collaboration and communication
  9. Testing and deployment hurdles
  10. Developing for a global market

Performance and latency issues

While cloud services are generally reliable in terms of availability and performance, service outages or performance issues can impact development efforts.

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