InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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/articles/ai-based-prose-programming-for-subject-matter-experts/en/smallimage/AI-based-prose-programming-small-1691410669692.jpg)
AI-Based Prose Programming for Subject Matter Experts: Will This Work?
In this article, author Markus Völter discusses the future of programming using Large Language Model (LLM) tools like ChatGPT and GitHub’s Copilot for prose-to-code generation. He also talks about what new approaches and language changes need to be in place to help non-programmers take advantage of the "program in prose" techniques.
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/articles/chatgpt-agent-C-plus-plus/en/smallimage/creating-your-own-AI-co-author-using-Cplusplus-small-1687936436760.jpg)
Creating Your Own AI Co-Author Using C++
While using ChatGPT through a web interface is one thing, creating your own autonomous AI tool that interfaces with ChatGPT via its API is a different story altogether. As strong proponents of C++, in this article we are going to present a GPT tool written in C++ to ease the pain of dealing with the daunting task of editing endless editorial comments.
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/articles/dfs-architecture-comparison/en/smallimage/comparative-analysis-GFS-Tectonic-JuiceFS-small-1687347930445.jpg)
Comparative Analysis of Major Distributed File System Architectures: GFS vs. Tectonic vs. JuiceFS
As storage needs continue to grow, traditional disk file systems have revealed their limitations. To address the growing storage demands, distributed file systems have emerged as dynamic and scalable solutions. In this article, we explore the design principles, innovations, and challenges addressed by three representative distributed file systems: Google File System (GFS), Tectonic, and JuiceFS.
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/articles/analytical-data-management-duckdb/en/smallimage/in-process-analytical-data-management-with-DuckDB-small-1686236227723.jpg)
In-Process Analytical Data Management with DuckDB
DuckDB is an open-source OLAP database for analytical data management that operates as an in-process database, avoiding data transfer overhead. Leveraging vectorized query processing and Morsel-Driven parallelism, the database optimizes performances and multi-core utilization for analytical data processing.
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/articles/gdpr-with-spring-and-aop/en/smallimage/easy-implementation-of-GDPR-with-aspect-oriented-programming-small-1684505109873.jpg)
Easy Implementation of GDPR with Aspect Oriented Programming
GDPR compliance should be a default feature in every application that handles PII (Personally Identifiable Information). Most organizations have an impression that GDPR is a luxury feature that needs special tools to implement. But, we can see that the frameworks and design patterns we already use in our everyday development can very well be used to implement the GDPR rules.
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AIOps: Site Reliability Engineering at Scale
AIOps can simplify and streamline processes which can reduce the mental burden on employees while improving communication and collaboration between departments.
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/articles/wonders-of-postgres-logical-decoding-messages/en/smallimage/logo-1677840597750.jpg)
The Wonders of Postgres Logical Decoding Messages
In this article, author Gunnar Morling discusses Postgres database's logical decoding function to retrieve the messages from write-ahead log, process them, and relay them to external consumers, with help of use cases like outbox, audit logs and replication slots.
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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.
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Understanding and Applying Correspondence Analysis
Customer segments, personality profiles, social classes, and age generations are examples of effective references to larger groups of people sharing similar characteristics. Correspondence analysis (CA) is a multivariate analysis technique that projects categorical data into a numeric feature space which captures most of the variability in the data by fewer dimensions.
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How I Contributed as a Tester to a Machine Learning System: Opportunities, Challenges and Learnings
Have you ever wondered about systems based on machine learning? In those cases, testing takes a backseat. And even if testing is done, it’s done mostly by developers themselves. A tester’s role is not clearly portrayed. Testers usually struggle to understand ML-based systems and explore what contributions they can make. This is a journey of assuring quality of ML-based systems as a tester.
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/articles/deep-learning-models-ai-interpretability-methods/en/smallimage/LOGO-1675867673135.jpg)
Understanding and Debugging Deep Learning Models: Exploring AI Interpretability Methods
ML interpretability refers to a user's ability to explain decisions made by an ML system. Interpretability increases confidence in the model, reduces bias, and ensures that model is compliant and ethical. In this article, author Andrew Hoblitzell discusses several methods of ML interpretability and dives deep into Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Values.
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/articles/design-proposal-autoscaling-stateful-systems/en/smallimage/logo-1674489368378.jpg)
Design Pattern Proposal for Autoscaling Stateful Systems
In this article, Rogerio Robetti discusses the challenges in auto-scaling stateful storage systems and proposes an opinionated design solution to automatically scale up (vertical) and scale out (horizontal) from a single node up to several nodes in a cluster with minimum configuration and interference of the operator.