Mark Ryan is the lead Data Scientist for an insurance company in Toronto. He walks us through the ins and outs of structured data, how to manage it, and how to build Machine Learning systems. His book walks the reader through building a system that predicts whether bus routes in Toronto will be late using public domain data.    Panel    Charles Max Wood    Guest    Mark Ryan    Sponsors    Machine Learning for Software Engineers by Educative.ioAudible.com    Links    OpenAIRasaKeras    Picks    Chuck - Podcast Growth SummitChuck-Most Valuable DevChuck- Wheel of Time by Robert JordanMark - Spitting ImageAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

ML 014: Deep Learning with Structured Data with Mark Ryan

Machine Learning is growing in leaps and bounds both in capability and adoption. Listen to our experts discuss the ideas and fundamentals needed to succeed as a Machine Learning Engineer.Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

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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!SponsorsChuck's Resume TemplateDeveloper Book ClubBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Terry RodriguezBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Adaptive Industry ML: Challenges, Automation, and Model Applications - ML 149

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.SponsorsChuck's Resume TemplateDeveloper Book ClubBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Lukas GeigerBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Harnessing Open Source Contributions in Machine Learning and Quantization - ML 148

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.SponsorsChuck's Resume TemplateDeveloper Book ClubBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Nick SchrockBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Data Platform Innovation: Navigating Challenges and Building a Unified Experience - ML 147

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!SponsorsChuck's Resume TemplateDeveloper Book ClubBecome a Top 1% Dev with a Top End Devs MembershipBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

The Science-Engineering Blend - ML 146

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.SponsorsChuck's Resume TemplateDeveloper Book ClubBecome a Top 1% Dev with a Top End Devs MembershipBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

ML 014: Deep Learning with Structured Data with Mark Ryan

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