Some thoughts from the creator of an open source machine learning platform focused on developers.
Machine learning has, historically, been the purview of data science teams. This makes it a bit counter-intuitive that we built Cortex, our open source ML infrastructure platform, primarily for software engineers.
Going all the way back to machine learning’s roots in the 1950s, the field has historically been research-focused—things like Arthur Samuel’s checkers-playing AI (1959) or IBM’s chess-playing Deep Blue (1988).
Starting around 2010, there was a renewed interest in deep learning, with major tech companies releasing breakthroughs. Projects like Google Brain, DeepMind, and OpenAI (among others) began publishing new, state-of-the-art results.
These breakthroughs manifested as features in big companies’ products:
- Netflix’s recommendation engine
- Gmail’s smart compose
- Facebook’s facial recognition tags
In addition, this renewed focus on machine learning—and particularly deep learning—lead to the creation of better tools and frameworks, like Google’s TensorFlow and Facebook’s PyTorch, as well as open source models and datasets, like OpenAI’s GPT-2 and ImageNet.
With better tools, open source models, and accessible data, it became possible for small teams to train models for production. As a consequence of this democratization, a wave of new products have emerged, all of which at their core are “just” ML models wrapped in software. We refer to these products as ML-native.