With AI's growing popularity, Maj explores how data centers, including those offered by TSSI, could benefit from the AI boom. He notes TSSI's main customer is likely Dell and emphasizes the company's strength in quickly deploying modular data centers. While TSSI doesn't produce AI-specific hardware, its rapid deployment capability is crucial for supporting AI-driven data center growth. Maj plans to consult industry experts for deeper insights and believes TSSI could gain recognition in the AI data center space.

TSSI's Role In AI Data Centers | PodClip

Welcome to *AI with Shaily,* hosted by Shailendra Kumar, your friendly guide to the fascinating world of artificial intelligence! 🤖✨ In this episode, Shaily dives into a powerful but often overlooked innovation in machine learning called feature stores—a centralized system that organizes and manages the "features," or key data elements, that fuel ML models. Imagine it as a well-stocked pantry 🍽️ where all your essential ingredients are neatly stored and easily accessible, making your cooking (or in this case, your ML projects) smooth and efficient.

Shaily explains that feature stores have become a game-changer in MLOps (machine learning operations) as of 2025. They serve as a single source of truth, eliminating duplicated efforts and inconsistencies when different teams build models. Having worked extensively in AI development, Shaily shares a personal story where introducing a feature store transformed collaboration—teams stopped reinventing similar features under different names, saving time and boosting trust in the data. This also ensured that features used during model training matched those used during real-time prediction, leading to faster and more reliable outcomes. ⚡️🔍

Beyond organization, feature stores bring automation and pipeline management to the table. They track every change with versioning and governance—think of it like a detailed history book 📚 for your data ingredients—and perform quality checks by spotting outliers and missing values before the data reaches the model. This rigor helps build more robust and dependable ML systems.

Scalability is another highlight. Feature stores are designed to handle massive data volumes and provide real-time access, making them essential for production environments. Industry leaders like Databricks and Hopsworks offer enterprise-grade platforms, while open-source tools like Feast make these capabilities accessible to a wider audience of engineers. 🌐🚀

Shaily poses a thoughtful question: How often do ML projects slow down because teams don’t share a common “feature language”? If that sounds familiar, feature stores could be the solution you need. As a bonus tip, Shaily advises choosing feature store solutions that integrate smoothly with your existing MLOps pipeline, ensuring seamless connection between feature engineering, model training, deployment, and monitoring—key to operational success. 🔗💡

To close, Shaily quotes AI pioneer Andrew Ng: “AI is the new electricity.” Just like electricity, AI’s true power depends on solid infrastructure—and feature stores are the wiring that energizes machine learning today. ⚡️🔌

Thanks for tuning in to *AI with Shaily!* Don’t forget to subscribe on YouTube, follow Shailendra Kumar on Twitter and LinkedIn, and explore his deep-dive articles on Medium. Share your thoughts on feature stores or any AI topic in the comments—Shaily loves engaging with the community! Until next time, keep innovating and remember: the future is feature-rich! 🌟📈

Feature Stores Uncovered: The Secret to Faster, Smarter Machine Learning

We introduce WINA (Weight-Informed Neuron Activation), a novel framework designed to enhance the efficiency of Large Language Models (LLMs) without requiring retraining. Traditional methods often struggle with the significant computational and memory demands of LLMs, leading to high costs and slow performance. WINA addresses these challenges by employing a dual-criterion approach for sparse activation, which intelligently selects neurons based on both their current signal strength and their potential downstream influence. This training-free optimization dramatically reduces inference costs and speeds up processing, offering substantial improvements in accuracy and efficiency compared to previous sparse activation techniques. Ultimately, WINA enables LLMs to operate more efficiently on various platforms, from cloud services to mobile devices, signaling a significant step toward more practical and cost-effective AI deployment.

Trimming the Neural Fat - How WINA Slims Down AI Without Retraining

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New tracks tagged #AIInfrastructure