Data, analytics, machine learning, and AI in healthcare in 2021
What do you get when you juxtapose two of the hottest domains today – AI and healthcare? A peek into the future, potentially.
Read More →Databases, graphs, and GraphQL: The past, present, and future
GraphQL was never conceived as a query language for databases. Yet, it's increasingly being used for this purpose. Here's why, and how.
Read More →IOTA still wants to build a better blockchain and get it right this time
In 2014, IOTA set out to offer an alternative to the key issues with blockchain: Scalability and transaction fees. Somewhere along the way, things went wrong. Not everything is lost, however, and IOTA is looking to regain momentum.
Read More →OctoML scores $28M to go to market with open source Apache TVM, a de facto standard for MLOps
The open source Apache TVM project is becoming a de facto standard in MLOps, and OctoML is gearing its commercialization and scale up
Read More →Streamlining data science with open source: Data version control and continuous machine learning
Can an open source-based workflow leveraging version control and continuous integration and deployment help streamline machine learning, like it did for software development?
Read More →Cutting-edge Katana Graph scores $28.5 million Series A Led by Intel Capital
Here's why and how a startup founded by a duo of researchers some months back is attracting big enterprise clients and serious funding
Read More →Off-chain reporting: Toward a new general purpose secure compute framework by Chainlink
The upgrade in Chainlink's oracle service could mean more data availability at lower cost, leading to more applications
Read More →Up and to the right: TigerGraph scores $105M Series C funding, the Graph market is growing
The largest funding round to date in the graph market is good news not just for TigerGraph, but for the market at large
Read More →Data meets science: Open access, code, datasets, and knowledge graphs for machine learning research and beyond
A new interconnected ecosystem for research is shaping up, and machine learning is just the tip of the iceberg.
Read More →AI chips in the real world: Interoperability, constraints, cost, energy efficiency, and models
The answer to the question of how to make the best of AI hardware may not be solely, or even primarily, related to hardware
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