Source: thenewstack.io
Industrialize Machine Learning to Minimize Technical DebtCategory: Business, Infrastructure, automation, artificial-intelligence
More and more organizations have realized just how critical these technologies are to their ability to remain competitive and they’re investing accordingly, with the vast majority of AI and ML budgets and staffs growing.
These organizations are moving very quickly to capitalize on the top-line and bottom-line opportunities machine learning creates for their businesses, aided by the proliferation of simplified tooling that lowers the barrier to entry.
This reflects the creativity and persistence of the data scientist: They’re willing to do whatever it takes to get their models in production, and they’re not afraid to get their hands dirty with operational tasks in order to achieve early wins.
Companies that industrialize ML are able to act much faster in response to the data-driven insights that their models produce.
And your data scientists get more of their time back to do what they do best: Build innovative, creative models that produce tangible business value.
These organizations are moving very quickly to capitalize on the top-line and bottom-line opportunities machine learning creates for their businesses, aided by the proliferation of simplified tooling that lowers the barrier to entry.
This reflects the creativity and persistence of the data scientist: They’re willing to do whatever it takes to get their models in production, and they’re not afraid to get their hands dirty with operational tasks in order to achieve early wins.
Companies that industrialize ML are able to act much faster in response to the data-driven insights that their models produce.
And your data scientists get more of their time back to do what they do best: Build innovative, creative models that produce tangible business value.
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