Google Cloud’s Machine Learning Startup Competition brings together the most promising young startups implementing machine learning.

Win prizes like a $500k investment opportunity from top VCs, $1M in GCP credit, technical support from Google engineers and visibility for your startup.

over $2,000,000 in prizes


Investment Prizes

Our partners Data Collective and Emergence Capital will each offer an opportunity for a winner to receive an investment of up to $500K. For complete details, see the official rules.*

“Built With Google” Prize

The winner will be awarded $1M in GCP credits, 1:1 technical support from Google, plus 10 G Suite licenses for 12 months**

“Built With Google” Runner Up

The runner-up will be awarded $500K in GCP credits, 1:1 technical support from Google, plus 10 G Suite licenses for 12 months**


All non-winning finalists receive $200k in GCP credit plus 10 G Suite licenses for 12 months.**

* All investment awards are pending due diligence by the sponsoring firm.
** All GCP credits are in addition to any credits the startup has previously received through accelerator or other channel partners.

Investment Partners

Supporting Partners


To be eligible to enter the Google Cloud Machine Learning Startup Competition, companies must meet the requirements of the official rules, including:

  • Incorporated in the US.
  • Raised less than $5M in funding.
  • Actively implementing machine learning in their existing product.
  • Use of Google Cloud and ML or TensorFlow are encouraged, but not required to be eligible for any prizes except the “Built With Google” prizes.***
*** For “Built With Google” prizes, products must be built in whole or in part on GCP as of March 9, 2017.


Calendar of timeline.  Applications open on March 10, 2017.  Applications close on April 16, 2017.  Finalists selected in June 2017.  Final pitch-off will be held in the summer of 2017.


Is the competition limited to any specific verticals?
No, we welcome applications from startups applying ML in any and all industries.
What does it mean to be “actively implementing machine learning”?
We're most excited about teams that have the necessary training data, have a working machine learning model and are using ML to improve their product or customer experience.