Anthill Inside 2019

Building good ML systems is not very unlike developing good software. Just as developing good software requires mastering not only programming theory, tools, and design patterns, but also the process of software development itself, building a good ML system entails familiarity with the ML application lifecycle. In this talk, we will discuss the various stages of ML application life cycle - problem formulation, data definitions, modeling, production system design &implementation, testing, deployment & maintenance, online evaluation & evolution, and some key learnings that are relevant for each of these stages.

Outline

Building good ML systems is not very unlike developing good software. Just as developing good software requires mastering not only programming theory, tools, and design patterns, but also the process of software development itself, building a good ML system entails familiarity with the ML application lifecycle. In this talk, we will discuss the various stages of ML application life cycle - problem formulation, data definitions, modeling, production system design &implementation, testing, deployment & maintenance, online evaluation & evolution, and some key learnings that are relevant for each of these stages.

Requirements

None

Speaker bio

Srujana is an independent machine learning researcher and consultant with over 15 years of experience. Till recently, she was the chief scientist of CuspEra, a software marketplace startup. Prior to that, she was a principal data scientist at Flipkart (Bangalore) and a volunteer for Ekstep, an education startup. She has been employed with the machine learning groups at Amazon (Bangalore), IBM Research (Bangalore/New Delhi/Almaden/Yorktown Hts), and Yahoo Research (Santa Clara). Srujana has published her work in several top-tier conferences and journals on data mining/machine learning and is the recipient of multiple best paper awards. She received her M.S. and Ph.D. from the University of Texas at Austin and her B. Tech. degree from IIT Madras.

Links

• https://developers.google.com/machine-learning/problem-framing/?utm_source=googleAI&utm_medium=card-image&utm_campaign=training-hub&utm_term=&utm_content=problem-framing
• https://cloud.google.com/blog/products/ai-machine-learning/making-the-machine-the-machine-learning-lifecycle
• https://developers.google.com/machine-learning/guides/rules-of-ml
• https://www.slideshare.net/SrujanaMerugu1/ml-basics-204380593

Slides

https://www.slideshare.net/SrujanaMerugu1/ml-application-life-cycle