In today's episode, Michael Berk and Ben Wilson dive deep into the intricacies of technical interviews for machine learning roles. They discuss the importance of assessing candidates' genuine knowledge of traditional and deep learning models and the value of being candid about one's expertise.

Michael Berk and Ben Wilson from Databricks are joined by Brooke Wenig, who has a fascinating background in distributed machine learning. Today’s conversation dives deep into the intersection of AI, environmental science, and career transitions. They explore how individuals like Michael transformed their careers from environmental science to AI, leveraging existing expertise in innovative ways. Ben shares insights on leaping from non-technical roles to data science by embracing automation with Python and machine learning. We tackle the critical shift in roles, the balance between education and hands-on experience, and the growing disparity between academia and industry. Brooke brings valuable perspectives on project scoping, from aligning success criteria to ensuring real-world value. The discussion revolves around augmenting existing roles with AI, common pitfalls, and transitioning proofs of concept to production. They also explore the practical applications of language models, the debate over open versus closed source models, and the future of AI in various industries. With a focus on collaboration, the traits of top data scientists, and the implications of integrating AI into non-tech fields, this episode is packed with insights and tips for anyone looking to navigate the exciting world of AI and machine learning. Join them as they delve into these topics and more, discussing the evolving landscape of AI and how it's shaping careers and industries alike.

Michael Berk and Ben Wilson join cybersecurity expert Daniel Miessler to delve into the cutting-edge world of AI and cybersecurity. They discuss the evolving tactics of attackers, from specialized targeting to AI-driven data collection. The episode tackles dynamic risk assessment, the arms race between attackers and defenders, and the role of open-source models in security.

Fernando Lopez is an AI Engineer at Google. They delve deep into the realms of machine learning, documentation challenges in open-source projects, and the transition from startup environments to tech giants like Google. They share their candid experiences with impostor syndrome, practical tips for continuous learning, and the nuances of scaling solutions in the dynamic tech landscape. Explore the nuances of software development, the complex interplay of learning strategies, and the realities of navigating large-scale organizations. Join them as the industry experts unravel the intricacies of prototyping, scaling challenges, and the value of hands-on experience in shaping successful tech careers. Get ready to immerse yourself in a wealth of knowledge and thought-provoking insights that underscore the essence of growth and innovation in the tech realm.

Deeksha Goyal is the Senior Machine Learning Engineer at Lyft and Michael Sun is the Staff Software Engineer at Lyft. They delve into the intricacies of machine learning and data-driven technology. In this episode, they explore the challenges and innovations in deploying models into production, particularly focusing on the real-world implications of ETA (Estimated Time of Arrival) modeling at Lyft. They share valuable insights, from the complexities of A/B testing and long-term impact assessment, to the dynamic nature of handling real-time data and addressing unpredictability in route predictions. Join them as they journey through the world of model deployment, bug identification, and career development within the fast-paced environment of Lyft's data-driven infrastructure.

Matt Van Itallie is the Founder & CEO at Sema. This episode covers a wide range of topics, from the impact of AI and machine learning on software development and educational systems, to the importance of code reviews and career advice in the tech industry. Matt Van Italy shares his diverse experiences in law, consulting, public schools, and the tech sector, emphasizing the value of using data to drive improvements. The conversation also touches on the use of GenAI tools in development and the need for organizations to embrace new technology to stay competitive. They also explore issues such as defense spending, career transitions, and the significance of investing in education and human capital.

Terry Rodriguez is the Co-Founder at Remyx AI. They discuss the challenges and opportunities in deploying and updating AI models for robotics, exploring the potential applications across various industries, and delving into the complexities of conducting experiments and controlling for interaction effects. You'll also hear from industry experts who have worked on recommender algorithms and enhancing content recommendations through experimental workflows and hypothesis testing. Get ready for an insightful and dynamic conversation about the latest developments in the ML landscape!

Lukas Geiger is a Deep Learning Scientist, open-source developer, and an astroparticle physicist. He shares his experience using machine learning to analyze cosmic ray particles and detect secondary particles. We explore the challenges and opportunities of open source as a business model, the potential of models for edge computing, and the importance of understanding open-source code. Join us as we delve into the intersection of physics, machine learning, and the intricate world of software development.

Nick Schrock is the Founder of Dagster Labs. He is also the Creator of Dagster and the Co-creator of GraphQL. They delve into the world of data engineering, software development, and ML orchestration. In today's episode, they explore the challenges and intricacies of standardizing data movement, handling data access in various systems, and migrating data across different platforms. They share insights on the importance of building a system that spans multiple data platforms, the decision-making process behind tool development, and the impact of lineage in managing and migrating data. Join them as they uncover the complexities of open-source projects, API evolution, and the future of data engineering.

Ben and Michael dive into the dynamic relationship between engineers and scientists in the realms of software engineering and physical science. They explore the differences and similarities between these roles, sharing valuable insights on the research and testing processes, the importance of thorough research, the value of teamwork, and the challenges of transitioning between engineering and science. With analogies, real-world examples, and expert perspectives, they shed light on the intricacies of these roles and the considerations for hiring scientists and engineers based on company size and market effects. Tune in for a thought-provoking discussion on finding the optimal path between efficiency and innovation in the world of technology and research!

Michael and Ben dive into the critical role of design in software development processes. They emphasize the value of clear and understandable code, the importance of thorough design for complex projects, and the need for comprehensive documentation and peer reviews. The conversation also delves into the challenges of handling complex code, the significance of prototype research, and the distinction between design decisions and implementation details. Through real-world examples, they illustrate the impact of rushed processes on project outcomes and the responsibility of tech leads in analyzing and deleting unused code. Join them as they explore how process and organizational culture contribute to successful outcomes in tech companies and why companies invest in skilled individuals who can work efficiently within established processes.

Michael and Ben share their insights on being called in to fix issues in production systems at the last minute. They stress the importance of asking questions to understand the context and navigate the political landscape, and caution against providing half-baked solutions. They also discuss the significance of understanding project goals, documenting decision-making processes, and providing guidance to the team to avoid building unnecessary and difficult-to-maintain systems. Stay tuned as they share their experiences and valuable advice for navigating complex projects and delivering meaningful solutions.

Ben and Michael dive into the world of machine learning operations (MLOps) and discuss the complexities of building a computer vision pipeline to detect fishing boats at ports. They unpack the intricacies of MLOps basics and the challenges of implementing an effective computer vision model for traffic optimization and data collection at ports. From discussing the importance of exploratory data analysis (EDA) and data cleaning for image classification to the intricacies of continuous integration and deployment, this episode provides invaluable insights into the practical application of machine learning in real-world scenarios.

Ben and Michael dive into the complex world of decision-making, transparency, and truth-seeking in professional settings. They share their insights on challenging decisions, navigating organizational hierarchies, and the importance of evidence-based arguments. From the intricacies of software development to the dynamics of leadership, they discuss the challenges and strategies for making informed decisions and seeking truth within organizations. Whether you're a tech lead, director, or aspiring leader, this episode offers valuable perspectives on humility, empathy, and effective communication in the fast-paced world of technology.

Davis King is the perception engineer at Aurora. They talk about Dlib, which makes real-world machine learning and data analysis applications. They delve into the complexities of CUDA extensions, software layering, and the critical role of accurate data in machine learning. Join them as they dissect the challenges and importance of creating well-structured software with clear APIs, the intricacies of real-time systems, and the impact of language choice on code complexity and maintenance.