The Transformative Power of Generative AI in Software Development: Lessons from Uber's Tech-Wide Hackathon
Adam Hudauber.com
The Transformative Power of Generative AI in Software Development: Lessons from Uber's Tech-Wide Hackathon
Why is Continuous Delivery for ML/AI hard(er)?
Since the challenge is not new and many valid solutions exist targeting traditional software projects, is there a reason to treat ML/AI systems any differently? Consider these three core challenges that are endemic in ML, AI, and data projects:
Since the challenge is not new and many valid solutions exist targeting traditional software projects, is there a reason to treat ML/AI systems any differently? Consider these three core challenges that are endemic in ML, AI, and data projects:
- Development and debugging cycles are more tedious due to c
How To Organize Continuous Delivery of ML/AI Systems: a 10-Stage Maturity Model | Outerbounds
code-bison generates code from natural language descriptions, with a max input of 4,096 tokens and an output of 2,048 tokens. codechat-bison is a chatbot that is fine-tuned to help with code-related questions. It has an input limit of 4,096 tokens and an output limit of 2,048 tokens. code-gecko suggests code completions.