Gradient Flow #27: 2021 Trends Report, the Edge, and ML in the Sciences

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This edition has 363 words which will take you about 2 minutes to read.

“It takes muscle to do hard things, and muscle gets built by doing hard things.” – Dan Rose.

Data Exchange podcast

  • Bringing AI and computing closer to data sources  I discuss the state of edge computing with Bruno Fernandez-Ruiz, CTO and cofounder of Nexar, Inc., a startup that uses dash cams powered by vision-based applications to improve driving and logistics.
  • Deep Learning in the Sciences    Bharath Ramsundar, created DeepChem, an open source project that aims to democratize deep learning for science. He is also the co-creator of MoleculeNet, a benchmark for molecular machine learning.

Free Report

At the beginning of each year, we take stock of the year’s technological developments in areas around big data, analytics, machine learning, and AI. Here are some of the topics we cover in our newly released 2021 report:

  • Tools for Building Machine Learning and AI applications
  • Data Management and Data Engineering
  • Cloud Computing
  • Emerging AI Trends


Featured Virtual Event

This is an exciting period for companies needing to build data platforms that enable them to analyze and use all of their data assets. If you want to learn about cloud data warehouses and new paradigms like the lakehouse and the data mesh, we highly recommend that you attend Datanova: a  free, virtual two-day conference designed to help companies unlock the value of all their data. For a program overview, see this recent post from Jenn Webb and me.

Machine Learning tools and infrastructure

[Image: Caesarea in Israel, by Ben Lorica.]


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