Show Notes

• (2:22) Carl talked about his early exposure to programming and his Bachelor’s degree in Computer Science at the University of Rochester in the late 90s.
• (5:12) Carl implemented his first fully connected, two-hidden-layer artificial neural network using the C programming language back in 2000 when using neural networks wasn’t nearly as cool as it is today.
• (8:00) Carl started his career as a software engineer at IBM, writing software for large-scale distributed systems and voice-dialog management system.
• (13:31) The first production machine learning system that Carl worked on is called Conversational Interaction Manager, which is a dialog management system for conversational mixed-initiative natural language applications. He brought up the challenges in DevOps and data quality.
• (20:05) The second production machine learning system that you worked on is called Smarter Campus, which is a project that enables staffing recommendations based on social networking, optimization, and text analytics.
• (27:16) Carl unpacked the evolution of his career at IBM, working on various leadership roles. In particular, he worked on IBM Bluemix, IBM’s cloud platform-as-a-service, with over 1 million registered users. He emphasized the importance of talking to customers and finding product-market fit.
• (33:01) Carl discussed his decision to pursue a Master’s degree in Computer Science at the University of Florida in the mid of his career.
• (35:24) Carl explained his research paper, which combines game theory and machine learning called “AmalgaCloud: Social Network Adaptation for Human and Computational Agent Team Formation.” The paper focuses on the relationship between network adaptation for candidate group participants and the performance of problem-solving groups.
• (40:50) Carl discussed his patent on learning ontologies for machine learning - which maps ontologies from data warehouses to computer systems.
• (47:00) Carl unpacked his 4-part blog series dated in 2016 that discusses server-less computing via tools such as Docker and Apache OpenWhisk.
• (52:58) Carl emphasized the importance of learning Docker to be productive as a Machine Learning practitioner.
• (55:02) Carl became a program manager at Google Cloud and helped manage the company’s efforts to democratize machine learning via the Advanced Solutions Lab in 2017.
• (59:07) Carl recalled his experience as an instructor at various machine learning boot camps.
• (01:01:44) Carl went over the growing popularity of semi-structured data, referring to his talk at Google’s 2018 Data Cloud Next event.
• (01:06:29) Currently, Carl is the CTO of CounterFactual AI, which works with various clients using tools such as PyTorch and AWS. He brought up an example of a food delivery application.
• (01:09:13) Carl went over his experience leading a workshop on Server-less Machine Learning with TensorFlow at the Reinforce AI Conference in Budapest last year.
• (01:10:52) Carl is writing a book with Manning called Server-less Machine Learning In Action. He explained that server-less tools help minimize the efforts to do MLOps.
• (01:13:47) Carl talked about the rise of PyTorch as a production-ready deep learning framework, as well as his preference for the PyTorch’s language design philosophy.
• (01:17:10) Carl shared his opinions on choosing different cloud platforms to host and run the server-less ML pipeline.
• (01:19:37) Carl described the data and tech community in Orlando, Florida.
• (01:21:53) Closing segment.

His Contact Information

• Website
• LinkedIn
• Twitter
• GitHub
• Google Scholar

His Recommended Resources

• “An Introduction to Natural Computation” by Dana Ballard
• IBM’s semiconductor facility FAB
• IBM Bluemix
• “Pattern Recognition and Machine Learning” by Christopher Bishop
• Docker
• PyTorch
• PyTorch Lightning
• Jurgen Schmidhuber (the father of LSTM)
• Solomon Hykes (Founder, CTO, and chief architect of Docker)
• Dana Ballard (professor of Computer Science at UT-Austin)
• Gang of Four Design Patterns (engineering book with object-oriented design theory and practice)

Serverless Machine Learning In Action

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