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 →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.
Read More →Machine learning at the edge: TinyML is getting big
Being able to deploy machine learning applications at the edge is the key to unlocking a multi-billion dollar market. TinyML is the art and science of producing machine learning models frugal enough to work at the edge, and it's seeing rapid growth.
Read More →Graphs as a foundational technology stack: Analytics, AI, and hardware
How would you feel if you saw demand for your favorite topic — which also happens to be your line of business — grow 1,000% in just two years’ time? Vindicated, overjoyed, and a bit overstretched in trying to keep up with demand, probably.
Read More →Superconductive scores $21M Series A funding to sustain growth of its Great Expectations open source framework for data quality
Ensuring data quality is essential for analytics, data science and machine learning. Superconductive's Great Expectations open source framework wants to do for data quality what test-driven development did for software quality
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