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AI, ML & Data Engineering

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.

Suruchi Shah
on Nov 11, 2024
AI, ML & Data Engineering

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.

Anthony Alford
on Sep 17, 2024
AI, ML & Data Engineering

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.

Tingyi Li
on Sep 17, 2024
AI, ML & Data Engineering

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.

Srini Penchikala Mandy Gu Namee Oberst Roland Meertens Anthony Alford Daniel Dominguez
on Sep 06, 2024
AI, ML & Data Engineering

Adding a Natural Language Interface to Your Application

In this article, author Ashley Davis discusses how to add a natural language interface to a chatbot application using OpenAI REST API. He also shows how to extend the chatbot by adding voice commands using MediaRecorder API and OpenAI's speech transcription API.

Ashley Davis
on Apr 02, 2024
AI, ML & Data Engineering

Unpacking How Ad Ranking Works at Pinterest

Aayush Mudgal describes how Pinterest serves advertisements. He discussed in detail how Machine Learning is used to serve ads at large scale. He went over ads marketplaces and the ad delivery funnel, the ad serving architecture, and two of the main problems: ad retrieval and ranking. Finally, he discussed some of the challenges and solutions for training and serving large models.

Anthony Alford
on Mar 26, 2024
Culture & Methods

Testing Machine Learning: Insight and Experience from Using Simulators to Test Trained Functionality

When testing machine learning systems, we must apply existing test processes and methods differently. Machine Learning applications consist of a few lines of code, with complex networks of weighted data points that form the implementation. The data used in training is where the functionality is ultimately defined, and that is where you will find your issues and bugs.

Martin Karsberg
on Mar 07, 2024
AI, ML & Data Engineering

Generative AI: Shaping a New Future for Fraud Prevention

This article explores how generative AI affects fraud detection by reducing false positives and dynamically adapting to changing fraud patterns. This combination offers a potent preventive solution when integrated with machine learning. The efficacy and scalability of fraud prevention initiatives are enhanced by this innovative approach.

Neha Narkhede
on Feb 15, 2024
AI, ML & Data Engineering

InfoQ AI, ML, and Data Engineering Trends Report - September 2023

In this annual report, the InfoQ editors discuss the current state of AI, ML, and data engineering and what emerging trends you as a software engineer, architect, or data scientist should watch. We curate our discussions into a technology adoption curve with supporting commentary to help you understand how things are evolving.

Roland Meertens Srini Penchikala Sherin Thomas Daniel Dominguez Anthony Alford
on Sep 06, 2023
Culture & Methods

Reducing Verification Lead Time by 50% by Lowering Defect Slippage and Applying AI/ML Techniques

Can we increase our flexibility? Can we increase our test coverage? Can we increase our efficiency? And is it possible to reduce our verification lead-time by 50%? One company challenged itself with these questions. This article explores two important “‘pillars”’ of their testing strategy: shifting left and using state-of-the-art techniques to support verification activities.

Paul Derckx
on Aug 21, 2023
Culture & Methods

Minimising the Impact of Machine Learning on our Climate

This article introduces the field of green software engineering, showing the Green Software Foundation’s Software Carbon Intensity Specification, which is used to estimate the carbon footprint of software, and discusses ideas on how to make machine learning greener. It aims to give you the tools to take an active part in the climate solution.

Sara Bergman
on May 30, 2023
Culture & Methods

Moving towards a Future of Testing in the Metaverse

In this article, Tariq King describes the metaverse concept, discusses its key engineering challenges and quality concerns, and then walks through recent technological advances in AI and software testing that are helping to mitigate these challenges. To wrap up, he shares some of his thoughts on the role of software testers as we move towards a future of testing in the metaverse.

Tariq King
on Feb 24, 2023