A wave of obsession for all-things machine learning (ML) has washed over the technology and business communities — and society more broadly — in the last several years, and understandably so; machine learning-enabled products and services can present myriad benefits to an organization— not least the ability to harness large swaths of data to make previously tedious tasks more easy and efficient.
Having a solid foundation for real-world ML is a major determinant of success for new initiatives, and is an exciting area of research and engineering in its own right, but the implementation of ML can even be challenging for organizations with mature engineering strength, and it goes without saying that there can be pitfalls and misconceptions in attempts to make the jump between machine learning research and ML in production environments. A frequently overshadowed and often under-appreciated aspect of getting it right is the infrastructure that enables robust, well-managed research and serves customers in production applications.
A key lever in setting the foundation for a successful ML program is building a culture and an atmosphere that allows you to trial these efforts at scale: accelerating the rate of scientific experimentation on the road to production and, ultimately, to business value. The cloud is an integral part of these efforts, and it can enable teams to develop and deploy well-governed, accurate ML models to high-volume production environments. Beyond production deployments, a solid infrastructure paves the way for large-scale testing of models and frameworks, allows for greater exploration of the interactions of deep learning tools, and enables teams to rapidly onboard new developers and ensure that future model changes do not have masked effects.
This article originally appeared on machinelearning.co. To read the full article, click here.
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