Distributed ML/AI design strategy serves as a potential solution as it solves two of the most notorious problems, performing near real time analytics on humongous high-speed data. Identify Machine Learning models and find means to integrate them in to existing Distributed computing architecture. First, since the data is large and fast paced it pushes ML closer to the source of data instead of bringing data from different sources at a central location and then applying ML. Second, it focuses on figuring out the right modelling approach combined with the right architectural design for effectively doing distributed ML processing. Distributed ML design strategy utilises best of both the worlds, Distributed Computing and Machine Learning algorithms. It brings along the advanced analytical and mathematical reasoning capabilities of Machine Learning and combine it with efficient distributed processing in the form of a group of machines either in a Big Data cluster or over a GPU farm or steaming edge devices or over IoT devices. It is more of a design thought process that enterprises should focus on while planning to leverage Distributed ML architecture for their business problems. About the speaker: Kuldeep Jiwani: Researcher, Data Scientist, Data Science Architect, Performance specialist, Entrepreneur. Currently working as a Data Science Researcher and Distinguished Architect in Thales (Guavus) handling various Data Science projects. Along with actively exploring new techniques of Machine Learning via various research projects with focus on Unsupervised ML. Thus ensuring that Data Science could be effectively applied to real world scenarios and able to solve important problems.

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Business Intelligence and Analytics
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