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Questions To Ask When Moving Machine Learning From Practice to Production

#artificialintelligence

With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings. Coupled with breakneck speeds in AI-research, the new wave of popularity shows a lot of promise for solving some of the harder problems out there. That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks. A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved. This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production.


Moving machine learning from practice to production

#artificialintelligence

With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings. Spending some time on planning your infrastructure, standardizing setup and defining workflows early-on can save valuable time with each additional model that you build. After building, training and deploying your models to production, the task is still not complete unless you have monitoring systems in place. Periodically saving production statistics (data samples, predicted results, outlier specifics) has proven invaluable in performing analytics (and error postmortems) over deployments.