Machine learning at the edge: A hardware and software ecosystem
Being able to deploy machine learning applications at the edge bears the promise of unlocking a multi-billion dollar market. For that to happen, hardware and software must work in tandem. Arm's partner ecosystem exemplifies this, with hardware and software vendors like Alif and Neuton working together.
Read More →DeepMind aims to marry deep learning and classic algorithms
Will deep learning really live up to its promise? We don’t actually know. But if it’s going to, it will have to assimilate how classical computer science algorithms work. This is what DeepMind is working on, and its success is important to the eventual uptake of neural networks in wider commercial applications.
Read More →The state of MLOps in 2021
MLOps is the art and science of bringing machine learning to production, and it means many things to many people. The State of MLOps is an effort to define and monitor this market.
Read More →Apollo GraphQL announces $130 Million Series D Funding, wants to define its own category
GraphQL is a specification that came at just the right time to address an age-old issue in software engineering: service integration. Apollo's implementation is seeing lots of traction, and it just got more gas in the tank for its grand vision that goes well beyond integration
Read More →AI ethics in the real world: FTC commissioner shows a path toward economic justice
FTC Commissioner Rebecca Kelly Slaughter lays out machine learning and AI's potential for harm and outlines some ways for the FTC to counter it.
Read More →Amazon Neptune update: Machine learning, data science, and the future of graph databases
Amazon Neptune just added another query language, openCypher, to its arsenal. That may not sound like a big deal in and of itself, but coupled with updates in machine learning and data science features, it points towards the future of graph databases.
Read More →Building MLGUI, user interfaces for machine learning applications
Machine learning is eating the world, and spilling over to established disciplines in software, too. After MLOps, is the world ready to welcome MLGUI (Machine Learning Graphical User Interface)?
Read More →Open-source growth and venture capital investment: Data, databases, challenges, and opportunities
Open-source software used to be poorly understood by commercial forces, and it's often approached in a biased way. A new generation of investment funds goes to show that things are changing.
Read More →More than words: Shedding light on the data terminology mess
Data management, data governance, data observability, data fabric, data mesh, DataOps, MLOps, AIOps. It's a data terminology mess out there. Let's try and untangle it, because there's more to words than lingo.
Read More →The biggest investment in database history, the biggest social network ever, and other graph stories from Neo4j
A $325 million Series F funding round, bringing Neo4j's valuation to over $2 billion. A social network of 3 billion people, distributed across 1000 servers. The latter is a demo; the former is not. But both are real signs that the graph market and Neo4j are getting huge.
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