InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
How to Use Open Source Prometheus to Monitor Applications at Scale
In this article, the author discusses how to collect metrics and achieve anomaly detection from streaming data using Prometheus, Apache Kafka and Apache Cassandra technologies.
-
Using Intel Analytics Zoo to Inject AI into Customer Service Platform (Part II)
This article shares the practical experience of building a QA ranker module on Azure’s customer support platform using Intel Analytics Zoo by Microsoft Azure China team. You can quickly learn step by step how to prepare data to train, evaluate and tune a text matching model at scale and finally productionize it as a service using Analytics Zoo.
-
How to Mitigate the Pain of Getting and Giving Feedback
Companies that encourage open and honest feedback do better than companies that do not. Nonetheless, giving feedback is difficult because social and physical pain share some of the same neural circuitry. Hence, feedback can feel physically painful, as Sarah Hagan discusses in her 2018 QCon San Francisco talk . Hagan uses scientific research to demonstrate how to give feedback properly.
-
Real-Time Data Processing Using Redis Streams and Apache Spark Structured Streaming
Structured Streaming, introduced with Apache Spark 2.0, delivers a SQL-like interface for streaming data. Redis Streams enables Redis to consume, hold and distribute streaming data between multiple producers and consumers. In this article, author Roshan Kumar walks us through how to process streaming data in real time using Redis and Apache Spark Streaming technologies.
-
Open Source Robotics: Getting Started with Gazebo and ROS 2
An introduction to Gazebo, a powerful robot simulator that calculates physics, generates sensor data and provides convenient interfaces, and ROS 2, the latest version of the Robot Operating System, which offers familiar tools and capabilities, while expanding to new use cases. Both are open source and used by academia and industry alike.
-
The Data Science Mindset: Six Principles to Build Healthy Data-Driven Organizations
In this article, business and technical leaders will learn methods to assess whether their organization is data-driven and benchmark its data science maturity. They will learn how to use the Healthy Data Science Organization Framework to nurture a data science mindset within the organization.
-
The Impact and Ethics of Conversational Artificial Intelligence
Improvements in natural language understanding and our changing relationship means we can use chatbots in ways we couldn’t before - both to augment human conversation and support, or indeed, to replace it. Those working in the software industry must understand and take responsibility for how we use Conversational AI and our users' data.
-
Test Automation in the World of AI & ML
An in-depth look at the criteria & requirements for Functional Test Automation in the agile world, and the capabilities you should build in your custom framework, or should exist the tools you choose. Anand Bagmar explores aspects like readability, reuse, debugging / rca, CI, Test Data, Parallel Execution, integration with other tools & libraries, free Vs open-source and support.
-
Increasing the Quality of Patient Care through Stream Processing
Today’s healthcare technology landscape is disaggregated and siloed. Physicians analyse patient data streams from different systems without much correlation. Even though health-tech domain is mature and rich with data, the value of it is not directed towards increasing the quality of patient care. This article presents a stream processing solution in which streams are co-related.
-
Scaling a Distributed Stream Processor in a Containerized Environment
The article presents our experience of scaling a distributed stream processor in Kubernetes. The stream processor should provide support for maintaining the optimal level of parallelism. However, adding more resources incurs additional cost and also it does not guarantee performance improvements. Instead, the stream processor should identify the level of resource requirement and scale accordingly.
-
Conquering the Challenges of Data Preparation for Predictive Maintenance
Predictive maintenance (PdM) applications aim to apply machine learning (ML) on IIoT datasets in order to reduce occupational hazards, machine downtime, and other costs. In this article, the author addresses some of the data preparation challenges faced by the industrial practitioners of ML and the solutions for data ingest and feature engineering related to PdM.
-
Book Review: Optimizing Java
InfoQ reviewed the book Optimizing Java, a comprehensive in-depth look at performance tuning in the Java programming language written by Java industry experts, Ben Evans, James Gough and Chris Newland. InfoQ spoke to the authors for more insights on their experiences, learnings and obstacles in authoring this book.
CONTENT IN THIS BOX PROVIDED BY OUR SPONSOR
An in-memory computing platform can add speed and scalability to your data-intensive applications
High Performance, Real-time Data Architecture for the Internet of Things
Learn how in-memory computing addresses the most common use cases and challenges associated with the real-time processing and storage of IoT-generated data.
In-Memory Computing Best Practices: Building a Foundation and Roadmap for Digital Transformation
In this eBook, you’ll learn best practices for establishing a sound and cost-effective in-memory computing foundation that focuses on the customer experience and more.
A Machine and Deep Learning Primer
Learn how in-memory computing can make machine and deep learning faster, while reducing complexity and expense.
Introducing the GridGain In-Memory Computing Platform White Paper
Improving Speed and Scalability with In-Memory Data Grids White Paper
451 Research Report: HOAP and In-Memory Computing: Accelerating Digital Business and Customer Experiences