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That's because putting machine learning research into production and using it to offer real value to customers is often harder than developing a scientifically sound algorithm.
From this experience, the mission for AWS Machine Learning is to put machine learning capabilities into the hands of every developer, regardless of their skills and experience level.
Integrate machine learning into products and services.
Google launched TensorFlow Serving today, a new open source project that aims to help developers take their machine learning models into production.
Translate machine learning into Japanese.
Applying machine learning in production systems.
The company uses machine learning to analyze production times and logistics and better predict delivery dates.
G.M. never put that car into production.
Lattice uses machine learning to essentially put that data into order and to make it more usable.
In the most general terms, the workflow for a supervised machine learning task consists of three phases: build the model, evaluate and tune the model, and then put the model into production.
(Seriously, put that thing into production).
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CEO of Professional Science Editing for Scientists @ prosciediting.com