Coffee Sessions #127

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00:00:00 - 01:00:00

In the video, Niall Murphy and Todd Underwood discuss the challenges of reliable machine learning and how it can help businesses of all sizes. They posit that the cross-functional nature of machine learning teams will be key to success, and that individual contributors should strive to grow into more mature roles.

00:00:00 This video features two interviewees, Nile Murphy and Todd Underwood, discussing their new book, "Reliable Machine Learning." Murphy and Underwood discuss the importance of culture in machine learning, and how books like this can help educate and inspire others in this field.
00:05:00 Nile and Todd discuss their experiences in the machine learning industry, Todd's book "Reliable Machine Learning," and their upcoming company, Stanza Systems.
00:10:00 In this video, Niall Murphy and Todd Underwood discuss running a machine learning SRE organization. Murphy shares that it has been harder than expected to get people investing in the future due to the current political climate. Underwood shares that one effective way to manage a team is to get the experienced team members one day per week to work on a video conference.
00:15:00 Niall Murphy and Todd Underwood discuss the idea of rewarding employees to keep them motivated and productive. They point out that it is difficult to motivate employees when their actions do not feel impactful. They also discuss the idea of process rewards, which are rewards that are tied to specific goals or objectives.
00:20:00 In this video, Niall Murphy and Todd Underwood discuss the importance of reliable machine learning and how it can help businesses of all sizes. Murphy cites the analogy of information technology in the 1970s, when computers were first introduced into enterprise, as an example of how machine learning will impact business in the future. He and Underwood posit that the cross-functional nature of machine learning teams will be key to success, and that individual contributors should strive to grow into more mature roles.
00:25:00 Niall Murphy and Todd Underwood discuss the role of machine learning in business, highlighting the importance of data and tooling. Murphy also discusses the challenges of leading a small startup, and how machine learning fits into that.
00:30:00 Staff engineers in larger organizations model behavior and set culture, and often have a broader scope than individual specialists. Machine learning is a good fit for this type of work because it allows for the intersection of different organizations and the best data and ideas happen at the interstices of these boundaries.
00:35:00 According to the speaker, reliability engineering is a difficult task because it is difficult to determine when an outage has been fixed. Additionally, machine learning can make the task of reliability engineering even more difficult, as it is difficult to determine when a model is "right." In order to properly perform reliability engineering, it is necessary to communicate with all of the people involved in the model's creation and use.
00:40:00 Niall Murphy and Todd Underwood discuss the challenges of reliability engineering, and how the community is evolving to meet those challenges. Niall warns that if the questions a reliability engineer is asking don't seem relevant or exciting, it might be time to get some help.
00:45:00 In this video, Niall Murphy and Todd Underwood discuss the risks and benefits of artificial intelligence in organizations. Murphy emphasizes the importance of transparency and trust in AI-driven decisions, and warns against using AI for racist or unethical purposes.
00:50:00 Niall Murphy and Todd Underwood discuss the potential for regionalization of machine learning in the world, with Murphy noting that he sees this as a result of cultural inertia. They also discuss the need for more understanding between policymakers and technologists, and the importance of preparing the next generation of practitioners.
00:55:00 In this video, Niall Murphy and Todd Underwood discuss how engineers can be responsible for the consequences of their creations, and how regulation or jail can incentivize people to act in a certain way. They also mention how character and culture can be strong forces in shaping how people act.

01:00:00 - 01:00:00

The presenters discuss their upcoming book, Reliable Machine Learning, which is due to be published within a week or two. They mention that the book is geared towards machine learning operators and provides advice and examples for using machine learning algorithms.