cloud

NetApp Extends Microsoft Alliance to Include CloudOps Tools

NetApp this week extended its alliance with Microsoft to now include its CloudOps portfolio of tools for optimizing cloud computing environments. Previously, the alliance between the two companies focused on data management but is now expanding to include tools to deploy workloads, improve performance and reduce costs using machine learning algorithms across both instances of virtual machines and the Azure Kubernetes Service (AKS). Kevin McGrath, vice president of Spot by NetApp, said in more challenging economic times, there’s a lot more focus on programmatically reining cloud costs using FinOps best practices within the context of a DevOps workflow. Organizations are also starting to create platform engineering teams to more efficiently manage DevOps workflows at scale across hybrid cloud computing environments, he added. For years, developers have been provisioning cloud infrastructure resources with little to no oversight. Unfortunately, developers are also prone to over-provision infrastructure resources to ensure maximum application availability. Many of those infrastructure resources never wind up being consumed by the application, so the cost of cloud computing winds up becoming inflated. IT leaders are also being increasingly required to make sure cloud costs are more predictable. Sudden spikes in consumption resulting in higher monthly bills are an unwelcome surprise to finance teams that are now required to manage costs more closely. Ongoing advances in artificial intelligence (AI) should make it easier to predict costs across highly dynamic cloud computing environments. Navigating all the pricing options that cloud service providers make available is challenging. IT teams need to clearly understand the attributes of each workload to ensure optimal usage of cloud infrastructure resources. Less clear is the degree to which IT teams are pitting cloud service providers against one another. Pricing across the cloud services that most organizations use today is fairly consistent. Most organizations that deploy workloads in the cloud tend to run the bulk of them on the same service because they lack the internal expertise needed to manage multiple clouds equally well. There may be some workloads running on additional clouds, but enterprise licensing agreements reward customers for running more workloads on a cloud. The only way to really optimize cloud spending is to shift workloads to less expensive tiers of service that might only be available for a relatively limited amount of time. One way or another, the management of cloud computing is finally starting to mature. As the percentage of workloads that organizations have running in the cloud steadily increases, IT teams are becoming more adept at both maximizing application performance and the associated return on investment (ROI). Each IT organization will need to decide for itself how best to manage cloud computing environments as it continues to build and deploy cloud-native applications alongside legacy monolithic applications running on virtual machines, but NetApp is betting the need for tools such as CloudOps will increase as cloud computing environment become more complex. The challenge, as always, is finding and retaining the talent needed to manage cloud computing environments when every other organization is looking for that same expertise.

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The Growing Impact of Generative AI on Low-Code/No-Code Development

No-code/low-code platforms, once a disruptor in the realm of software development, are now embracing the capabilities of generative AI to create even more dynamic experiences. This union of convenience and innovation redefines how users interact with their software. Imagine a scenario where crafting complex instructions like “Deploy endpoint protection to noncompliant devices” becomes as simple as conversing with your application. The fusion of generative AI and no-code/low-code platforms empowers users to shape their software’s behavior without delving into intricate technicalities. Users can input prompts such as “Generate a code snippet for converting date formats” or “Create a workflow that automates inventory updates.” By translating natural language into action, this approach streamlines development and fosters creativity. An Amalgamation of Generative AI and No-Code/Low-Code Beyond buzzwords, the amalgamation of generative AI with no-code/low-code platforms offers tangible benefits. The efficiency gains that occur when users can sidestep the need for manual configurations and directly communicate their intentions are both remarkable and unprecedented. Accessibility is enhanced, enabling non-technical individuals to actively participate in application development. Moreover, innovative use cases emerge, allowing organizations to streamline complex workflows with ease. As with any transformative technology, challenges emerge alongside benefits. Privacy concerns loom large when dealing with data input into generative AI models. Striking a balance between providing valuable insights and safeguarding sensitive information becomes paramount. Additionally, the inherently non-deterministic nature of generative AI can lead to varying outcomes, requiring careful consideration of use cases to ensure reliable results. As this collaboration matures, the landscape of software development is poised for significant change. Conversational interfaces that empower users to dictate software behaviors will continue to evolve, reducing implementation and configuration overhead. Imagine a future where complex workflows are summoned with a simple request or applications are custom-built based on natural language blueprints. This shift will not only streamline development but also democratize technology, making it accessible to a broader audience. The integration of generative AI with no-code/low-code platforms allows users to express their creativity more freely. By enabling natural language prompts like “Design an app to manage inventory with automatic restocking” or “Build a workflow that offboards a user across Google, Slack, and Salesforce,” users can drive software behaviors without being constrained by technical jargon. This fusion redefines the efficiency of software interaction. Tasks that previously required meticulous configuration or coding can now be executed through simple prompts. Whether generating email templates, creating data transformation scripts, or orchestrating multi-step workflows, the convenience of natural language input eliminates barriers and accelerates results. A Democratic Approach Looking forward, the integration of generative AI in no-code/low-code platforms points toward a more democratic approach to software development. This convergence will enable a broader range of individuals to participate actively, regardless of their coding expertise. By simplifying the process and making it more inclusive, we’re shaping a future where software truly adapts to human intent. As businesses continue to harness the potential of generative AI and no-code/low-code platforms, adaptation and learning will be key. Embracing this transformation requires a shift in mindset, and understanding that software can be molded through conversations and prompts. As technology matures, the barriers between user intent and software behavior will fade, ushering in an era where technological fluency is defined by our ability to communicate rather than code. Speculating on how this shift will impact the day-to-day […]

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