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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.
Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
Multi-tenant architecture allows software vendors to realize tremendous efficiencies by maintaining a single application stack instead of separate database instances while meeting data privacy needs. When you use a data warehouse to power your multi-tenant analytics, the proper approach is vital. Multi-tenant analytics is NOT the primary use case with traditional data warehouses, causing data security challenges.
Apache Cassandra is an open-source distributed database that boasts an architecture that delivers high scalability, near 100% availability, and powerful read-and-write performance required for many data-heavy use cases. However, many developers and administrators who are new to this NoSQL database often encounter several challenges that can impact its performance.
Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.
“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.
There’s a good reason why Apache Cassandra® is quickly becoming the NoSQL database of choice for organizations of all stripes. In this white paper, discover the key use cases that make Cassandra® such a compelling open source software – and learn the important pitfalls to avoid. From understanding its distributed architecture to unlocking its incredible power for industries like healthcare, finance, retail and more, experience how Cassandra® can transform your entire data operations.
In embedded analytics, keeping up with the pace of innovation is challenging. Download Qrvey's guide to ensure your analytics keep pace so you can solve your user's biggest challenges, delight them, and set your product apart from the competition. The guide outlines how to use embedded analytics to: Increase user satisfaction Go to market faster Create additional opportunities to monetize your product It also shares what to look for to ensure your embedded analytics are keeping up with the lates
Join our exclusive webinar with top industry visionaries, where we'll explore the latest innovations in Artificial Intelligence and the incredible potential of LLMs. We'll walk through two compelling case studies that showcase how AI is reimagining industries and revolutionizing the way we interact with technology. Some takeaways include: How to test and evaluate results 📊 Why confidence scoring matters 🔐 How to assess cost and quality 🤖 Cross-platform cost vs. quality tr
In today's digital landscape, the threat of ransomware demands proactive defense. This paper, inspired by a real PostgreSQL® database incident, offers vital strategies for effective mitigation. Instaclustr expert Perry Clark outlines immediate actions to minimize risks, ensuring a swift response to ransomware threats and protecting critical data assets.
Are you thinking of adding enhanced data matching and relationship detection to your product or service? Do you need to know more about what to look for when assessing your options? The Senzing Entity Resolution Buyer’s Guide gives you step-by-step details about everything you should consider when evaluating entity resolution technologies. You’ll learn about use cases, technology and deployment options, top ten evaluation criteria and more.
Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. They're often developing using prompting, Retrieval Augmented Generation (RAG), and fine-tuning (up to and including Reinforcement Learning with Human Feedback (RLHF)), typically in that order. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are le
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