O’Reilly survey results show that AI efforts are maturing from prototype to production, but company support and an AI/ML skills gap remain obstacles.
AI & ML
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
Our annual analysis of the O’Reilly online learning platform reveals Python’s continued dominance and important shifts in infrastructure, AI/ML, cloud, and security.
O’Reilly survey highlights the increasing attention organizations are giving to data quality and how AI both exacerbates and alleviates data quality issues.
Edward Jezierski on the science of bringing creativity and curiosity together in a learning system.
Roger Magoulas looks at developments in automation, hardware, tools, model development, and more that will shape (or accelerate) AI in 2020.
Rob Thomas and Tim O’Reilly discuss the AI Ladder framework.
Understanding and fixing problems in ML models is critical for widespread adoption.
It’s clear that AI can and will have a big influence on how we develop software.
Dean Wampler discusses the challenges and opportunities businesses face when moving AI from discussions to production.
Ankur Patel discusses challenges and opportunities in enterprise machine learning and AI applications.
Eric Jonas on AI hype and questions of ethics.
We need to remember that creating fakes is an application, not a tool—and that malicious applications are not the whole story.
Experts explore TensorFlow 2.0's machine learning capabilities as well as the broader tools and applications of TensorFlow.
Ankur Narang discusses sticker recommendations with multilingual support, a key innovation driven by sophisticated natural language processing (NLP) algorithms.
Anna Roth discusses human and technical factors and suggests future directions for training machine learning models.
Tony Jebara explains how Spotify improved user satisfaction by building components of the TFX ecosystem into its core ML infrastructure.
Sandeep Gupta and Joseph Paul Cohen introduce the TensorFlow.js library.
Konstantinos Katsiapis and Anusha Ramesh dive into the insights and approach that helped TensorFlow Extended (TFX) reach its current popularity within Alphabet.
Chris Lattner and Tatiana Shpeisman explain how MLIR addresses the complexity caused by software and hardware fragmentation.
Mike Liang discusses TensorFlow Hub, a platform where developers can share and discover pretrained models and benefit from transfer learning.
Jared Duke and Sarah Sirajuddin explore on-device machine learning and the latest updates to TensorFlow Lite.
Theodore Summe offers a glimpse into how Twitter employs machine learning throughout its product.
Megan Kacholia explains how Google’s latest innovations provide an ecosystem of tools for developers, enterprises, and researchers who want to build scalable ML-powered applications.
Jeff Dean explains why Google open-sourced TensorFlow and discusses its progress.