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Explore integrating generative AI into real-time streaming pipelines, covering enterprise use cases, model selection, and implementation using tools like NiFi, Flink SQL, and cloud services.
Explore how LLMs revolutionize developer relations, enhance product learning, and create AI-powered documentation tools. Learn to build custom LLM apps for improved developer experiences.
Explore LLM observability maturity in AWS, from foundational concepts to advanced AIOps, covering metrics, performance, security, and best practices for effective implementation.
Explore mechanistic interpretability and superposition in LLM feature representations, focusing on neural network qualities, linearity, and sparsity to uncover hidden features and improve model understanding.
Explore techniques to measure and mitigate hallucinations in Retrieval-Augmented Generation (RAG) systems, with practical examples and insights on improving LLM-based applications.
Explore real-time data correction in LLM applications using vector indexing. Learn to build a chatbot that adapts to changing information, ensuring accuracy and reliability in AI-powered conversations.
Explore challenges and solutions for integrating AI chatbots in business, focusing on practical implementation and overcoming common obstacles.
Explore LLM vulnerabilities, attacks, and defenses. Learn about prompt injections, output handling, and jailbreaking. Discover secure practices for AI implementation and see real-world examples.
Explore practical strategies for securing AI applications, from responsible AI principles to vulnerability control and evaluation techniques. Learn to implement guardrails and observability in generative AI projects.
Explore the process of running open-source LLMs, covering infrastructure, deployment, costs, and key learnings for effective implementation in real-world scenarios.
Explore how GenAI and data drive innovation, growth, and personalized experiences. Learn about RAG, vector embeddings, and building data foundations for AI applications.
Explore LLMs' effectiveness in detecting data anomalies, understand data quality importance, and learn practical techniques for anomaly detection using OpenAI and BigQuery.
Develop an LLM vulnerability scanner to audit and secure AI applications. Learn about LLM security risks and build a CLI tool for detecting vulnerabilities in AI systems.
Explore operational excellence for LLMs using Amazon Bedrock. Learn design principles, LLMOps, model lifecycle, customization, and architectural patterns for effective implementation and evaluation.
Explore Hono, a versatile web framework for edge computing. Learn about JavaScript runtimes, WinterCG standards, and Hono's key features like flexibility, speed, type safety, and middleware support.
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