As more businesses are turning towards ML and as data is available in abundance, there is a high demand of adopting ML in the industry. This brings the focus to the right choice of ML tools for both development and operationalising ML in production. The ML tools ecosystem has responded well, with a wide plethora of tools to solve various problems associated with ML. This wide variety of choices for ML possess a new challenge to the ML users, on when to use which technology. The most significant challenge comes in productionising ML, as for real world use cases the challenges are manyfold like how to do ML over Big Data, do ML over high speed streaming data, light weight ML over edge devices, distributed ML over high speed clusters, fast ML supported with machine acceleration technologies, etc. We will try to address a high level overview of the landscape and walk through some of the popular industry cases where choosing the right tool in production becomes a challenge.

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10:00 - 11:00 hs GMT+1

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Business Intelligence and Analytics
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