February 27, 2023

ML Building Blocks

Open-source software has helped developers build new products without needing to reinvent the wheel each time they create something new. These building blocks, accessible to every developer in rich ecosystems like npm and RubyGems, created a fertile ground for better and more interesting products, by allowing developers to focus their time on the most creative parts of building software. 

Machine learning models have the potential to follow a similar path. By releasing intuitive, easy-to-use ML models into the development world, we enable engineers to push the boundaries of how and where AI fits into our digital world.

There are roughly two orders of magnitude more software engineers than there are machine learning engineers (~30 million vs. ~500,000). By building good tools, we think it is possible for software engineers to use machine learning in the same way they can use normal software.

You should be able to import an audio transcriber the same way you an import an npm package. You should be able to fine-tune GPT as easily as you can fork something on GitHub.

Twenty years ago people might have said they were going to build an “internet application”. We don’t say that any longer. The internet is just the way that things are done.

Soon enough, that’s what machine learning will be like. It’ll just be how software is done. There’ll be a sprinkling of intelligence in everything. And it’s going to be built by software engineers.

Machine learning needs better tools, Replicate

Funding

Earthly, a tool for building fast and repeatable CI/CD pipelines, raised $6.5m in Seed+ funding.

Replicate, a platform for running and scaling machine learning models in the cloud, raised $12.5m in Series A funding.

Finch, a single API for employment data, including payroll and benefits, raised $40m in Series B funding.