Subhash Talluri, AWS on "Data Science on AWS"

 

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Subhash Talluri is a specialist solutions architect in AI/ML working for AWS. He brings cross-functional expertise at the intersection of engineering; cloud computing, machine learning, computer science and business to provide quality and scalable solutions.

He'd like to position himself as a full stack data scientist but well knowing that such unicorns do not exist he is pursuing an alternative strategy. There are essentially five elements to a data scientist: data engineering, data visualization, machine learning, big data and cloud computing. He aims to be a specialist in two of these areas (machine learning, cloud computing) and a generalist in the other three. This allows him to handle the complete data lifecycle. From identifying the right problem, translating the problem in terms of data, getting the data, building the pipelines, analysing data, building models, presenting findings and putting models in production, he aspires to handle it all. 

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Data Science on AWS

AWS has been continually expanding its service portfolio to support virtually any cloud workload, including many services and features in the area of artificial intelligence. In the context of data science projects on AWS, the benefits of cloud computing include agility, cost savings, elasticity, faster innovation and smooth transition from prototype to production. Amazon SageMaker is a fully managed offering that addresses every aspect of machine learning by its modular design. All machine intelligence is powered by data. However, not all data are created equal. We need to critically evaluate machine-learning products from a standpoint that prioritizes the quality of the data streaming into them. This necessitates the need for a data lake or a data platform with considerations for horizontal scalability, a single source of truth, data governance and appropriate security frameworks.