Small Teams Are Shipping ML Features Faster Than Ever

Managed machine learning platforms and pre-trained models have lowered the barrier to shipping a real ML feature — teams no longer need a dedicated infrastructure function just to get a model into production.

The trade-off is that the hard part has shifted from infrastructure to data quality, evaluation and monitoring: a feature that works in a notebook can still fail in production if the underlying data drifts or edge cases aren’t tested.

Our Data Science & Machine Learning Bootcamp covers this end-to-end, including a deployment module so you can see a model go from a dataset to a working API.