The state of AI in 2021: Machine learning in production, MLOps and data-centric AI
With lessons learned from operationalizing AI, the emphasis is shifting from shiny new models to perhaps more mundane, but practical aspects such as data quality and data pipeline management
It’s that time of year again: Reports on the state of AI for 2021 are out. A few days back, it was the Machine learning, Artificial Intelligence and Data report by Matt Turck, that ZDNet Big on Data colleague Tony Baer covered. This week, it’s the State of AI 2021 report, by Nathan Benaich and Ian Hogarth.
After releasing what probably was the most comprehensive report on the State of AI in 2020, Air Street Capital and RAAIS founder Nathan Benaich and AI angel investor and UCL IIPP visiting professor Ian Hogarth are back for more.
In what is becoming a valued yearly tradition, we caught up with Benaich and Hogarth to discuss topics that stood out for us in the report.
First off, there is overlap with the topics that Turck covered and Baer reported on, and for good reason. As Baer pointed out, the wave of IPOs and proliferation of unicorns is turning this market into its own sector, and that is impossible to ignore. For an overview of market trends, we encourage readers to have a look at Baer’s coverage.