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Home / 2021 / March

Data, analytics, machine learning, and AI in healthcare in 2021

  • ganadiotis
  • Mar 30, 2021
  • AI / Machine Learning, Analytics, Data Science, Featured, Interview, Knowledge Graphs, News, Technical, Use Case
  • No comments yet
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.

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Databases, graphs, and GraphQL: The past, present, and future

  • ganadiotis
  • Mar 30, 2021
  • Analysis, Databases, Graph, Interview, Open Source, Technical
  • No comments yet
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.

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IOTA still wants to build a better blockchain and get it right this time

  • ganadiotis
  • Mar 22, 2021
  • Blockchain, Business, Featured, Interview, News, Open Source, Technical
  • No comments yet
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.

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OctoML scores $28M to go to market with open source Apache TVM, a de facto standard for MLOps

  • ganadiotis
  • Mar 17, 2021
  • Business, Interview, MLOps, News, Open Source, Technical
  • No comments yet
OctoML scores M 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

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Streamlining data science with open source: Data version control and continuous machine learning

  • ganadiotis
  • Mar 03, 2021
  • Data Science, Interview, News, Open Source, Technical
  • No comments yet
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?

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