Today we speak with ex-Googler, Praveen Paritosh. He has over 20 years of experience as a research scientist and has worked on some of AI's most impactful projects. Expect to learn about scientific innovation, the importance of data, and the next wave of AI (spoiler, it's not LLMs).SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Praveen ParitoshAdvertising 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.

The Innovation Cycle of AI - ML 116

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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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.

The Impact of Process on Successful Tech Companies - ML 145

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.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.

Delivering Scoped Solutions: Lessons in Fixing Production System Issues - ML 144

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.SponsorsChuck's Resume TemplateDeveloper Book ClubBecome a Top 1% Dev with a Top End Devs MembershipAdvertising 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.

The Innovation Cycle of AI - ML 116

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