Where I express personal thoughts in words
Last month, I attended another apply(meetup), Tecton’s follow-up virtual event of their ML data engineering conference series. For context, I have written recaps for both of their 2021 events, including the inaugural conference and the follow-up meetup. The content below covers my learnings, ranging from model calibration and ranking systems to real-time analytics and online feature stores.
As Chief Business Development Officer, Taimur is responsible for developing emerging businesses at Redis and leading strategic business & corporate development. He is currently leading initiatives related to AI/ML.
Prior to Redis, Taimur led Worldwide Customer Success for Microsoft's Azure Data & AI. He jointly led the design, implementation, and landing of one of Microsoft's largest field transformations, which combined customer success, support engineering, and technical account management.
Before Microsoft, Taimur was the Managing Director for Amazon Web Services (AWS) Platform Technology and Applications - where he led business development from 2008 (near its inception) to 2018 when the business reached $25B in ARR. Taimur helped forge key partnerships and customers, including Airbnb, CapitalOne, Dropbox, Liberty Mutual, NASA JPL, Nasdaq, Netflix, Nintendo, Intuit, SAP, and Samsung.
Taimur grew up in three countries and lived in five states. Bellevue, WA is home for him, where he lives with his wife and three boys. Taimur enjoys cross-training, hiking, and biking. He is an avid reader of technology, business, and history. He enjoys art, music, coffee, and cooking on the weekends for his family.
This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.
