InfoQ Homepage Machine Learning Content on InfoQ
-
/articles/test-automation-ai-ml/en/smallimage/test-automation-ai-ml-s-1549400167942.jpeg)
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.
-
/articles/infoq-editor-recommended-reading-part-two/en/smallimage/infoq-editor-recommended-reading-part-two-s-1545062692174.jpeg)
The 2018 InfoQ Editors’ Recommended Reading List: Part Two
As part of our core values of sharing knowledge, the InfoQ editors were keen to capture and share our book and article recommendations for 2018, so that others can benefit from this too. In this second part we are sharing the final batch of recommendations
-
/articles/machine-learning-learn-devops/en/smallimage/machine-learning-learn-devops-s-1544716403154.jpeg)
What Machine Learning Can Learn from DevOps
The fact that machine learning development focuses on hyperparameter tuning and data pipelines does not mean that we need to reinvent the wheel or look for a completely new way. According to Thiago de Faria, DevOps lays a strong foundation: culture change to support experimentation, continuous evaluation, sharing, abstraction layers, observability, and working in products and services.
-
/articles/analytics-zoo/en/smallimage/analytics-zoo-s-1544204138443.jpeg)
Analytics Zoo: Unified Analytics + AI Platform for Distributed Tensorflow, and BigDL on Apache Spark
In this article we described how Analytics Zoo can help real-world users to build end-to-end deep learning pipelines for big data, including unified pipelines for distributed TensorFlow and Keras on Apache Spark, easy-to-use abstractions such as transfer learning and Spark ML pipeline support, built-in deep learning models and reference use cases, etc.
-
/articles/sentiment-analysis-whats-with-the-tone/en/smallimage/sentiment-analysis-whats-with-the-tone-s-1543275808291.jpeg)
Sentiment Analysis: What's with the Tone?
Sentiment analysis is widely applied in voice of the customer (VOC) applications. In this article, the authors discuss NLP-based Sentiment Analysis based on machine learning (ML) and lexicon-based approaches using KNIME data analysis tools.
-
/articles/natural-language-processing-with-java-book-review/en/smallimage/natural-language-processing-with-java-book-review-1539035529033.jpg)
Natural Language Processing with Java - Second Edition: Book Review and Interview
Natural Language Processing with Java - Second Edition book covers the Natural Language Processing (NLP) topic and various tools developers can use in their applications. Technologies discussed in the book include Apache OpenNLP and Stanford NLP. InfoQ spoke with co-author Richard Reese about the book and how NLP can be used in enterprise applications.
-
/articles/codefirst-future-ui-design/en/smallimage/codefirst-future-ui-design-s-1538375682680.jpeg)
Codefirst: The Future of UI Design
User interface design has played a critical role in computing for decades. Flat and tactile design are current trends in application design. Voice user interfaces are emerging with Alexa, Siri, and Google. Augmented and virtual reality, and IoT lead to significant changes in designs. AI is poised to create significant changes by perfecting user interface designs.
-
/articles/machine-learning-unconscious-bias/en/smallimage/analyzing-and-preventing-unconscious-bias-in-machine-learning-s-1534197829508.jpeg)
Analyzing and Preventing Unconscious Bias in Machine Learning
This article is based on Rachel Thomas’s keynote presentation, “Analyzing & Preventing Unconscious Bias in Machine Learning” at QCon.ai 2018. Thomas talks about the pitfalls and risk the bias in machine learning brings to the decision-making process. She discusses three use cases of machine learning bias.
-
/articles/system-behaviour-time-series-ml/en/smallimage/software-system-ml-1532598838952.jpeg)
Understanding Software System Behaviour with ML and Time Series Data
David Andrzejewski presented "Understanding Software System Behaviour with ML and Time Series Data". This article is a summary of his presentation and an overview on what to look out for. Know about the traditional approaches to time series, how to handle missing values, and know about possibly occurring seasonality in your data. Be careful about what threshold you set for anomaly detection.
-
/articles/Can-People-Trust-Algorithm-Decisions/en/smallimage/logo-1529438072056.jpeg)
Can People Trust the Automated Decisions Made by Algorithms?
The use of automated decision making is increasing. These algorithms can produce results that are incomprehensible, or socially undesirable. How can we determine the safety of algorithms in devices if we cannot understand them? Public fears about the inability to foresee adverse consequences has impeded technologies such as nuclear energy and genetically modified crops.
-
/articles/most-training-data-svm/en/smallimage/logo-small-1514382242232.jpg)
Get More Bytes for Your Buck
Lovethesales had to classify one million product data from 700 different disparate sources across a large domain. They decided to create a hierarchy of classifiers through utilizing machine learning, specifically Support Vector Machines. They learned that optimising the way in which the svms were connected together yielded vast improvements in the reuse of labeled training data.
-
/articles/problem-with-ai/en/smallimage/logo-ai.jpeg)
The Problem with AI
AI depends on "data janitorial" work, as opposed to science work, and there is a gulf between prototype and sandbox, and innovation and production.