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“Death by 1,000 Pilots” Tim O’Reilly and Mike Loukides AI & ML – Radar
[[{“value”:” Most companies find that the biggest challenge to AI is taking a promising experiment, demo, or proof-of-concept and bringing it to market. McKinsey Digital Analyst Rodney Zemmel sums this up: It’s “so easy to fire up a pilot that you can get stuck in… Read More »“Death by 1,000 Pilots” Tim O’Reilly and Mike Loukides AI & ML – Radar
Context Serialization Mike Loukides AI & ML – Radar
[[{“value”:” In a recent edition of The Sequence Engineering newsletter, “Why Did MCP Win?,” the authors point to context serialization and exchange as a reason—perhaps the most important reason—why everyone’s talking about the Model Context Protocol. I was puzzled by this—I’ve read a lot of… Read More »Context Serialization Mike Loukides AI & ML – Radar
Customize Amazon Nova models to improve tool usage Baishali Chaudhury AWS Machine Learning Blog
[[{“value”:” Modern large language models (LLMs) excel in language processing but are limited by their static training data. However, as industries require more adaptive, decision-making AI, integrating tools and external APIs has become essential. This has led to the evolution and rapid rise of agentic… Read More »Customize Amazon Nova models to improve tool usage Baishali Chaudhury AWS Machine Learning Blog
Evaluate Amazon Bedrock Agents with Ragas and LLM-as-a-judge Rishiraj Chandra AWS Machine Learning Blog
[[{“value”:” AI agents are quickly becoming an integral part of customer workflows across industries by automating complex tasks, enhancing decision-making, and streamlining operations. However, the adoption of AI agents in production systems requires scalable evaluation pipelines. Robust agent evaluation enables you to gauge how well… Read More »Evaluate Amazon Bedrock Agents with Ragas and LLM-as-a-judge Rishiraj Chandra AWS Machine Learning Blog
VernamVeil: A Fresh Take on Function-Based Encryption Vasilis Vryniotis Datumbox
Cryptography often feels like an ancient dark art, full of math-heavy concepts, rigid key sizes, and strict protocols. But what if you could rethink the idea of a “key” entirely? What if the key wasn’t a fixed blob of bits, but a living, breathing function?… Read More »VernamVeil: A Fresh Take on Function-Based Encryption Vasilis Vryniotis Datumbox
Advanced Techniques to Build Your RAG System Muhammad Asad Iqbal Khan MachineLearningMastery.com
This post is divided into three parts; they are: • Query Expansion and Reformulation • Hybrid Retrieval: Dense and Sparse Methods • Multi-Stage Retrieval with Re-ranking One of the challenges in RAG systems is that the user’s query might not match the terminology used in… Read More »Advanced Techniques to Build Your RAG System Muhammad Asad Iqbal Khan MachineLearningMastery.com
10 Python One-Liners for Machine Learning Modeling Iván Palomares Carrascosa MachineLearningMastery.com
Building machine learning models is an undertaking which is now within everyone’s reach. Building machine learning models is an undertaking which is now within everyone’s reach. Read More
Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale Renuka Kumar, Toby Fotherby, Shweta Keshavanarayana, Thomas Matthew, Daniel Vaquero, Atul Varshneya, and Jessica Wu AWS Machine Learning Blog
[[{“value”:” This blog post is co-written with Renuka Kumar and Thomas Matthew from Cisco. Enterprise data by its very nature spans diverse data domains, such as security, finance, product, and HR. Data across these domains is often maintained across disparate data environments (such as Amazon… Read More »Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale Renuka Kumar, Toby Fotherby, Shweta Keshavanarayana, Thomas Matthew, Daniel Vaquero, Atul Varshneya, and Jessica Wu AWS Machine Learning Blog