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19 posts

How to avoid cloud whiplash

“Cloud whiplash” refers to the challenges and rapid changes organizations face while adopting and managing cloud computing infrastructures. The term captures the often disorienting and disruptive shifts as enterprises try to keep up with rapidly evolving technologies, varied cloud services, and changing business requirements.Cloud whiplash is usually due to frequent changes in cloud strategies, resulting in inefficiencies, security vulnerabilities, and operational disruptions. An example would be the security team needing a cloud security plan when applications are redeployed in a public cloud. The stress of not having the work done or the resources to do the job is the foundation of cloud whiplash.  To read this article in full, please click here
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How to choose a data analytics and machine learning platform

Analytics platforms have evolved considerably over the last decade, adding capabilities that extend far beyond the last generation’s on-premises reporting and business intelligence (BI) tools. Modernized data visualization, dashboarding, analytics, and machine learning platforms serve different business use cases, end-user personas, and data complexities.  While analytics platforms have reached mainstream adoption, many businesses in lagging industries want to develop their first dashboards and predictive analytics capabilities. They recognize that managing analytics in spreadsheets is slow, error-prone, and hard to scale, while using reporting solutions tied to one enterprise system can be limiting without integrations to other data sources.To read this article in full, please click here
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LlamaIndex review: Easy context-augmented LLM applications

“Turn your enterprise data into production-ready LLM applications,” blares the LlamaIndex home page in 60 point type. OK, then. The subhead for that is “LlamaIndex is the leading data framework for building LLM applications.” I’m not so sure that it’s the leading data framework, but I’d certainly agree that it’s a leading data framework for building with large language models, along with LangChain and Semantic Kernel, about which more later.To read this article in full, please click here
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Does AI make us dependent on Big Tech?

European banking executives are increasingly concerned about the growing dependence of big U.S. tech firms on integrated artificial intelligence (AI) in financial services. Substantial computing power is needed for AI, and many banks believe they will struggle to operate AI independently. They fear a shift towards “Big Tech” and “Big Cloud” due to AI adoption requirements.These concerns were widely discussed at a recent fintech conference in Amsterdam. I wish I had been there to remind them that they have direct control of the technology they deploy, and large companies and cloud providers don’t have a monopoly on AI. My best advice would be to calm down and remain objective enough to recognize there’s a reasonable path forward.To read this article in full, please click here
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Microsoft Fabric evolves from data lake to application platform

If there’s one thing a modern business needs, it’s data—as much of it as possible. Starting with data warehouses and now with data lakes, we’re using on-premises and cloud tools to manage and analyze that data, putting it in shape to deliver necessary business insights.Data is increasingly important today, as it’s now used to train and fine-tune custom AI models, or to provide essential grounding for existing AI applications. Microsoft’s Fabric is a hosted analytics platform that builds on top of existing data tools like Azure Synapse, so it’s not surprising that Microsoft used its AI-focused BUILD 2024 event to unveil new features that are targeted at supporting the at-scale analytics and data requirements of modern AI applications.To read this article in full, please click here
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4 highlights from EDB Postgres AI

35% of enterprise leaders will consider Postgres for their next project, based on internal research conducted by EDB, which also revealed that out of this group, the great majority believe that AI is going mainstream in their organization. Add to this, for the first time ever, analytical workloads have begun to surpass transactional workloads.Enterprises see the potential of Postgres to fundamentally transform the way they use and manage data, and they see AI as a huge opportunity and advantage. But the diverse data teams within these organizations face increasing fragmentation and complexity when it comes to their data. To operationalize data for AI apps, they demand better observability and control across the data estate, not to mention a solution that works seamlessly across clouds.To read this article in full, please click here
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Multicloud: Oracle links database with Google, Microsoft to speed operations

Oracle is connecting its cloud to Google’s to offer Google customers high-speed access to database services. The move comes just nine months after it struck a similar deal with Microsoft to offer its database services on Azure. Separately, Microsoft is extending its Azure platform into Oracle’s cloud to give OpenAI access to more computing capacity on which to train its models.“What started as a simple interconnect is becoming a more defined multicloud strategy for Oracle. The announcement is the beginning of a new trend—cloud providers are willing to work together to serve the needs of shared customers,” said Dave McCarthy, Research Vice President at IDC.To read this article in full, please click here
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A CISO game plan for cloud security

As businesses increasingly migrate to the cloud, chief information security officers (CISOs) face numerous critical challenges in ensuring robust cloud security. Don’t believe me? Experts highlighted this at the recent Gartner Security & Risk Management Summit. Gartner projects a significant 24% increase in spending on cloud security, positioning it as the fastest-growing segment within the global security and risk management market.Adapt, adjust, execute The bottom line is that shifting to cloud computing necessitates fundamentally rethinking security. Organizations strive to integrate the cloud into standard business operations, however, this transition has more pitfalls than most CISOs understand. I’ve seen this in my research and my experience as a consultant for 20 years, cloud and prior.To read this article in full, please click here
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Kubernetes shows the way forward for AI

The term “inflection point” is overused, but it certainly applies to the current state of artificial intelligence. Technology providers—and the companies that depend on them—can choose one of two roads to AI development: proprietary or open source. This dichotomy has existed for decades, with both sides achieving great levels of success. However, I would argue that the stakes for AI are higher than we’ve ever seen, and that the open source model is critical for the productive, economically feasible, and safe productization and consumption of AI.To read this article in full, please click here
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