Neural-backed generators are a promising step toward practical program synthesis. There’s a lot of hype surrounding AI, but are companies…
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. In…
AI and machine learning will require retraining your entire organization
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. In this episode of the…
What are model governance and model operations?
A look at the landscape of tools for building and deploying robust, production-ready machine learning models. By Ben Lorica, Harish…
The quest for high-quality data
Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. By Ihab Ilyas and Ben Lorica.…
AI adoption is being fueled by an improved tool ecosystem
We now are in the implementation phase for AI technologies. In this post, I share slides and notes from a…
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 a machine learning company means investing in foundational technologies
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and…
