AI and systems at RISELab
Ion Stoica outlines a few projects at the intersection of AI and systems that UC Berkeley's RISELab is developing.
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).
The future of machine learning is tiny
Pete Warden digs into why embedded machine learning is so important, how to implement it on existing chips, and some of the new use cases it will unlock.
The future of hiring and the talent market with AI
Maria Zheng examines AI and its impact on people’s jobs, quality of work, and overall business outcomes.
Tools for machine learning development
The O'Reilly Data Show: Ben Lorica chats with Jeff Meyerson of Software Engineering Daily about data engineering, data architecture and infrastructure, and machine learning.
RISELab’s AutoPandas hints at automation tech that will change the nature of software development
Neural-backed generators are a promising step toward practical program synthesis.
How to become a truly data-driven organization
From basic BI to using AI to automate and augment human endeavors, data-driven systems are increasingly powerful and pervasive in the enterprise.
Iterative competition is a catalyst for better problem solving
Kaggle is more than a machine learning competition platform; it’s a facilitator for efficient problem solving and a community for sharing and learning.
One simple chart: Who is interested in Spark NLP?
As we close in on its two-year anniversary, Spark NLP is proving itself a viable option for enterprise use.
Get ready to retrain your organization for AI and ML
To successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce.
Enabling end-to-end machine learning pipelines in real-world applications
The O’Reilly Data Show Podcast: Nick Pentreath on overcoming challenges in productionizing machine learning models.
What are model governance and model operations?
A look at the landscape of tools for building and deploying robust, production-ready machine learning models.
The quest for high-quality data
Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge.
AI needs a training reboot
Artificial intelligence is harder than we think--we have a long way to go to reach anything like general AI.
AI is an opportunity to redefine entire ecosystems
Jeff Wong on choosing to embrace change rather than feeding the dialogue of fear.
Improved tools are fueling AI adoption
We now are in the implementation phase for AI technologies.
Maximizing paper clips
We won’t get the chance to worry about artificial general intelligence if we don’t deal with the problems we have in the present.
Bringing scalable real-time analytics to the enterprise
The O’Reilly Data Show Podcast: Dhruba Borthakur and Shruti Bhat on enabling interactive analytics and data applications against live data.
Applications of data science and machine learning in financial services
The O’Reilly Data Show Podcast: Jike Chong on the many exciting opportunities for data professionals in the U.S. and China.
Becoming an ML company means investing in foundational tech
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms.
AI, ML, and a better customer experience
From data quality to personalization, to customer acquisition and retention, and beyond, AI and ML will shape the customer experience of the future.
Real-time entity resolution made accessible
The O’Reilly Data Show Podcast: Jeff Jonas on the evolution of entity resolution technologies.
Rise of the (advertising) machines
Mike Tidmarsh looks at how data and AI are radically reshaping the world of marketing communications.
Privacy, identity, and autonomy in the age of big data and AI
Sandra Wachter argues that a right to reasonable inferences could protect against new forms of discrimination.
Sustaining ML in the enterprise
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.