Cloud-Native Data Science: Turning Data-Oriented Business Problems Into Scalable Solutions
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The proliferation of Data Science is largely due to: ubiquitous data, increasing computational power and industry acceptance that solutions are an asset.
Data Science applications are no longer a simple dataset on a single laptop. In a recent project, we help develop a novel cloud-native machine learning service. It is unique in that problems are packaged as containers and submitted to the cloud for processing. This enables users to distribute and scale their models easily.
This talk will discuss the Data Science explosion and how that has altered the way engineers work. It will find that the boundaries between Data Science and Software Engineering are becoming even more blurred and techniques born out of Software Engineering can drastically improve many aspects of Data Science.
Through a demonstration of business-focused examples, we will follow the process of turning a business problem into a scalable solution. It will make heavy use of containers to smooth the development and productisation of a model.
This talk will be enjoyed by all and technical details will also be available for those who are interested.
Phil masters the hard art of backing theory by real-life experience. There is always more to learn about data and reinforcement learning... #data