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5 Real-Time Data Processing and Analytics Technologies – And Where You Can Implement Them

Seattle Data Guy

Real-time data processing can satisfy the ever-increasing demand for… Read more The post 5 Real-Time Data Processing and Analytics Technologies – And Where You Can Implement Them appeared first on Seattle Data Guy.

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Big Data Technologies that Everyone Should Know in 2024

Knowledge Hut

Big data in information technology is used to improve operations, provide better customer service, develop customized marketing campaigns, and take other actions to increase revenue and profits. In the world of technology, things are always changing. In this blog post, we will discuss such technologies.

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Stream processing models

Waitingforcode

If you're interested in stream processing, I bet your thinking is technology-based. It's not wrong, after all, the ability to use a tool gives you and me a job. However, for a long-term consideration it's better to reason in terms of patterns or models. Being aware of a more general vision helps assimilate new tools.

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Intelligent Document Processing: Technology Overview

AltexSoft

The documents often come in semi-structured and unstructured data formats, which makes them difficult to process quickly and accurately. That’s when intelligent document processing or IDP enters the game. This post is a perfect place to get acquainted with intelligent document processing.

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New Study: 2018 State of Embedded Analytics Report

Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.

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Latest Technology Trends 2024: Check the Biggest Upcoming Tech

Knowledge Hut

Technology has come a long way. With UPI, even a vegetable vendor or a small business owner can access high-end new technology (Newest tech). Top 10 Latest Technology Trends in 2024 It is no surprise that the demand for tech-savvy professionals is increasing as the world gets digital.

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Precisely Women in Technology: Meet Elizabeth

Precisely

The Precisely Women in Technology (PWIT) program was first established to create a space for women in the organization to come together. Each month, a member from the network is selected to participate in a Q&A to share more about her experience as a woman in technology. Why did you choose to pursue a career in technology?

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Putting the right LLMOps process in place today will pay dividends tomorrow, enabling you to leverage the part of AI that constitutes your IP – your data – to build a defensible AI strategy for the future.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. 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.

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.