All topicsSTATE OF AI REPORT. 2020

Why did MLOps matter in 2020?

By Nathan Benaich and Ian Hogarth · 2020 report

It addressed the work of deploying and operating models, reflecting a shift from research prototypes toward dependable production systems.

Why did MLOps matter in 2020? - 2020 report, PDF page 111
Why did MLOps matter in 2020? 2020 report, PDF page 111

How to read this finding

GitHub growth is an adoption signal, not revenue. Funding figures cover selected larger rounds and were projected from partial-year data; they should not be compared directly with broader startup totals.

Read the full AI business section

Evidence you can use

AI business in the 2020 report

Historical snapshot: October 2020. Dates and populations are specified per row.

AI business in the 2020 report
MeasureReported valueDefinition and source
ML infrastructure share of fast-growing projects25% of the top 20Q2 2020 GitHub growth comparison cited in the report.2020 report, PDF page 111
Projected qualifying funding volume>$25BAnnualized 2020 estimate for private AI-first rounds above $15M.2020 report, PDF page 129
Projected qualifying deal count350+Same selected round population and annualized estimate.2020 report, PDF page 129

GitHub growth is an adoption signal, not revenue. Funding figures cover selected larger rounds and were projected from partial-year data; they should not be compared directly with broader startup totals.

Data & methodology Download CSVJSON

Sources and dates

Historical snapshot published October 1, 2020. This web edition was prepared on 2026-10-11 from the online deck and original launch posts. Findings and forecasts retain their original time frame.

  1. 2020 report, PDF page 111Original 2020 report. This edition has no printed slide numbers. References use one-based PDF pages.
  2. 2020 report, PDF page 115Original 2020 report. This edition has no printed slide numbers. References use one-based PDF pages.
  3. 2020 report, PDF page 129Original 2020 report. This edition has no printed slide numbers. References use one-based PDF pages.
  4. State of AI Report 2020: online slides
  5. Nathan Benaich’s launch essayOctober 1, 2020.

Cite this page

Benaich, Nathan, and Ian Hogarth. “Why did MLOps matter in 2020?” State of AI Report 2020. Historical report snapshot; web edition prepared 2026-10-11.