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The future of data: A 5-pillar approach to modern data management

CIO

This article proposes a methodology for organizations to implement a modern data management function that can be tailored to meet their unique needs. Data science was previously the domain of tech-savvy organizations due to the technical expertise required to build models from scratch. However, there is good news for smaller companies.

Data 167
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Sesamm bags $37M to give corporates ESG insights using natural language processing

TechCrunch

Companies can access Sesamm’s flagship product, TextReveal , via several conduits, including an API that brings Sesamm’s NLP engine into their own systems. Elsewhere, private equity firms can use Sesamm for due diligence on potential acquisition or investment targets.

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Why GreenOps will succeed where FinOps is failing

CIO

Capital One built Cloud Custodian initially to address the issue of dev/test systems left running with little utilization. Architects must combine functional requirements with multiple other long-term requirements to build sustainable systems. The rapid adoption of AI is making the challenge an order of magnitude worse.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

Demystifying RAG and model customization RAG is a technique to enhance the capability of pre-trained models by allowing the model access to external domain-specific data sources. Unlike fine-tuning, in RAG, the model doesnt undergo any training and the model weights arent updated to learn the domain knowledge.

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How to Use Generative AI and LLMs to Improve Search

TechEmpower CTO

While traditional search systems are bound by the constraints of keywords, fields, and specific taxonomies, this AI-powered tool embraces the concept of fuzzy searching. One of the most compelling features of LLM-driven search is its ability to perform "fuzzy" searches as opposed to the rigid keyword match approach of traditional systems.

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Taming the cost of AI: Is FinOps the answer?

CIO

This article delves into developing FinOps solutions tailored for AI services, highlighting the unique considerations and strategic approaches necessary. For AI services, this implies breaking down costs associated with data processing, model training and inferencing. The focus is on enhancing cost visibility and control.

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Four challenges with ERP vendor-led AI roadmaps and how to solve them

CIO

AI requires a shift in mindset Being in control of your IT roadmap is a key tenet of what Gartner calls composable ERP , an approach of innovating around the edges which often requires a mindset shift away from monolithic systems and instead toward assembling a mix of people, vendors, solutions, and technologies to drive business outcomes.