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Data science with Python


There is a scenario that is quite common when doing data science at scale. The Data Science team have developed a good algorithm that suits our purpose and the prototype works well on a test dataset. But how to transform it into a reliable, responsive service ready for production payload? We will got through the steps involved in the evolution of a Jupyter notebook into an auto-scaling service. These steps involve changes in data ingestion, asynchronous processing, dockerisation, kubernetes and cloud technologies.


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