#31 DoK Community: The Data Lifecycle - Where Do We Go From Here // Benjamin Rogojan. (Presenter: Bart Farrell)


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โดย Bart Farrell และถูกค้นพบโดย Player FM และชุมชนของเรา -- ลิขสิทธิ์นี้เป็นของผู้เผยแพร่ ไม่ใช่ Player FM โดยมีการสตรีมเสียงโดยตรงจากเซิร์ฟเวอร์ผู้เผยแพร่ กดปุ่มติดตามเพื่อติดตามการอัพเดทใน Player FM หรือวาง URL ฟีดนี้ไปยังแอพพอดคาสท์อื่น

Abstract of the talk…

Going from raw data to machine learning models successfully in companies of all sizes requires more than just an understanding of programming. Teams need to manage their data products lifecycle, their software as well as the data. Data products like machine learning models aren’t created out of thin air. They are built on layers of best practices that ensure the models are using accurate data, they are outputting reliable numbers and they have some method to interact with the outside world. So how do we get there? The purpose of this talk is to discuss the current state of the data lifecycle as it pertains to creating data products. This could be machine learning models, dashboards and data APIs. We will outline the general architecture that helps take data from raw to some form of machine learning model. In addition, we will discuss some of the concepts that are being applied from DevOps as well as being created in MLOps to help better facilitate your data life cycle.


Ben has spent his career focused on all forms of data. He has focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. He has also worked in various industries including transportation, Big Tech, start-ups, insurance, Saas and more. In all of these industries he has helped companies develop their data strategy. Often starting from scratch to develop an end-to-end data solution. Ben privately consults on data science and engineering problems both solo with Seattle Data Guy as well as with a company called Acheron Analytics. He has experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

Key take-aways from the talk…

- Creating successful data products and models requires more than just programming skills - Best practices from DevOps can help improve data science and ML models maintenance and lifecycle

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