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The AI Tipping Point: What Manufacturing Leaders Need to Know for 2025

AI is proving that it’s here to stay. While 2023 brought wonder, and 2024 saw widespread experimentation, 2025 will be the year that manufacturing enterprises get serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

SwiftKV from Snowflake AI Research Reduces Inference Costs of Meta Llama LLMs up to 75% on Cortex AI

Large language models (LLMs) are at the heart of generative AI transformations, driving solutions across industries — from efficient customer support to simplified data analysis. Enterprises need performant, cost-effective and low-latency inference to scale their gen AI solutions. Yet, the complexity and computational demands of LLM inference present a challenge. Inference costs remain prohibitive for many workloads. That’s where SwiftKV and Snowflake Cortex AI come in.

Best Automation Testing Companies in New York for Agile Businesses

Techniques that support agility have now assumed prominence in project management due to their ability to increase the probability of success. The current statistics reveal that the Agile project success rate is at 64% while the Waterfall project success rate is only at 49%. This is a prominent difference that shows how the Agile team performs successfully in fulfilling several project constraints. In terms of financial considerations, Agile makes a huge difference.

How to Display Charts on a Dashboard Like a Catalog

Yellowfin provide extensive customization for the design of dashboard content so you can achieve the exact look and feel required for your unique business audience and use case. Out-of-the-box, our drag-and-drop design canvas (Yellowfin Canvas) and low-code, no-code user interface enables non-developers to easily access many handy features. However, sometimes, you may want to extend Yellowfin further, or control the design and layout of your dashboard in a more specific way.

Benchmarking llama.cpp on Arm Neoverse-based AWS Graviton instances with ClearML

By Erez Schnaider, Technical Product Marketing Manager, ClearML In a previous blog post, we demonstrated how easy it is to leverage Arm Neoverse-based Graviton instances on AWS to run training workloads. In this post, we’ll explore how ClearML simplifies the management and deployment of LLM inference using llama.cpp on Arm-based instances and helps deliver up to 4x performance compared to x86 alternatives on AWS. (Want to run llama.cpp directly?

Demo | Snowflake Data Clean Rooms

Snowflake Data Clean Rooms empower organizations to collaborate on data in a privacy-conscious way directly within Snowflake. With an intuitive interface and a focus on simplifying secure data sharing, Snowflake Data Clean Rooms enables businesses to build and use clean rooms seamlessly, leveraging Snowflake’s powerful data platform. This solution eliminates unnecessary complexity and additional access fees, ensuring organizations can focus on deriving insights while maintaining data privacy. Learn more about how Snowflake Data Clean Rooms support privacy-preserving collaboration in this blog.

Optimizing Supply Chains with Data Streaming and Generative AI

It’s a truism that global supply chains are complex. The process of sourcing raw materials, transforming them into finished products, and distributing them to customers encompasses numerous systems (e.g., ERPs, WMSs, and TMSs). All systems within “the supply chain” are trending in the same direction; they’re aiming to be more efficient, resilient, and agile. Various technological developments have facilitated this directional trend.

Why shift-right testing brings real results

Shift-left testing gets all the attention, but it’s shift-right that reveals what truly works. At @BlaBlaCar, Quality Assurance Manager @Rémy Gronencheld explains why testing in production is critical for real-world success: Shift-left: Build with confidence but rely on assumptions. Shift-right: Test against the unpredictable—low connections, real devices, and user behavior. The reality? Combining both approaches lets teams take calculated risks without sacrificing quality.