InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Efficient Resource Management with Small Language Models (SLMs) in Edge Computing
Small Language Models (SLMs) bring AI inference to the edge without overwhelming the resource-constrained devices. In this article, author Suruchi Shah dives into how SLMs can be used in edge computing applications for learning and adapting to patterns in real-time, reducing the computational burden and making edge devices smarter.
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Being a Responsible Developer in the Age of AI Hype
Justin Sheehy emphasizes that AI is code, not magic, and warns against inflated claims about AI capabilities. He urges developers to approach AI with healthy skepticism, seeking verifiable evidence and focusing on ethical practices, including addressing bias, privacy, and data integrity. Clear communication about AI’s limitations and accountable use are essential to prevent hype and misuse.
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Virtual Panel: What to Consider When Adopting Large Language Models
Four experts discuss some issues people should think about when adopting LLMs and how they can make the best choice for their specific use case. Topics include how to choose between an API-based vs. self-hosted LLM, when to fine-tune an LLM, how to mitigate LLM risks, and what non-technical changes organizations need to make when adopting LLMs.
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Navigating LLM Deployment: Tips, Tricks, and Techniques
This article focuses on self-hosted LLMs and how to get the best performance from them. The author provides best practices on how to overcome challenges due to model size, GPU scarcity, and a rapidly evolving field.
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Article Series: Practical Applications of Generative AI
Generative AI (GenAI) has become a major component of the artificial intelligence (AI) and machine learning (ML) industry. However, using GenAI comes with challenges and risks. In the InfoQ "Practical Applications of Generative AI" article series, we present real-world solutions and hands-on practices from leading GenAI practitioners.
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Llama 3 in Action: Deployment Strategies and Advanced Functionality for Real-World Applications
This article details the enhanced capabilities of the open-source Llama 3 LLM, and how businesses can adopt the model in their applications. The author gives step-by-step instructions for deploying Llama 3 in the cloud or on-premise, and how to leverage fine-tuned versions for specific tasks.
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InfoQ AI, ML and Data Engineering Trends Report - September 2024
InfoQ editorial staff and friends of InfoQ are discussing the current trends in the domain of AI, ML and Data Engineering as part of the process of creating our annual trends report.
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The AI Revolution Will Not Be Monopolized
Large language models have significantly transformed the field of artificial intelligence. The fundamental innovation behind this change is surprisingly straightforward: make the models a lot bigger. With each new iteration, the capabilities of these models expand, prompting a critical question: are we moving toward a black box era where AI is controlled by a few tech monopolies?
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Using Generative AI in Software Project Management to Bridge Domains and Accelerate Productivity
Gen AI Assistants play to the strengths of professionals with a breadth of experience, particularly software developers who can describe what they want the LLM to complete and critically evaluate the result. These tools enable us to swiftly cross divides of domain language and scale large repetitive tasks down to interesting ones on a human scale.
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Optimizing Spring Boot Config Management with ConfigMaps: Environment Variables or Volume Mounts
Spring Boot stands out as a viable framework for its agility and streamlined workflow. Yet, effective configuration management remains a pivotal factor influencing deployment efficiency and ongoing maintenance. ConfigMaps, a feature in Kubernetes, provides configuration strategies for Spring Boot applications.
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Experimenting with LLMs for Developer Productivity
This article describes an experiment that sought to determine if no-cost LLM-based code generation tools can improve developer productivity. The experiment evaluated several LLMs by generating unit tests for some open-source code and measuring the code coverage as well as the manual rework necessary to make the tests work.
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Modernizing Testing Practices for Jakarta EE Projects
This article focuses on the increasing adoption of data-driven testing in Java enterprise applications and sheds light on the Data and NoSQL Jakarta specifications. It highlights the significance of modern testing libraries such as JUnit Jupiter and AssertJ and emphasizes the importance of container-based frameworks like Testcontainers in enhancing testing practices.