Functional Programming Shift and Scalable Architecture Insights - ML 158

Today, host Michael Berk and Ben Wilson dive deep into the multifaceted world of software engineering and data science with their insightful guest, Sandy Ryza a lead engineer from Dagster Labs. In this episode, they explore a range of intriguing topics, from the impact of the broken windows theory on code quality to the delicate balance of maintaining backward compatibility in evolving software projects. Sandy talks about the challenges and learnings in transitioning from data science back to software engineering, including dependency management and designing for diverse use cases. They touch on the importance of clear naming conventions, tooling, and infrastructure enforcement to maintain high code quality. Plus, they discuss the intricate process of selecting and managing Python libraries, the satisfaction of refactoring old code, and the necessity of balancing new feature development with stability.Michael and Ben will guide us through these essential discussions, emphasizing the significance of user-centric API design and the benefits of open source software. They also get practical advice on navigating API changes and managing dependencies effectively, with real-world examples from Dagster, Spark Time Series, and the libraries Numba and Pydantic.Join them for an episode packed with valuable insights and strategies for becoming a top-end developer! Don’t forget to follow Sandy on Twitter and check out Dagster.io for more information on his work.SocialsLinkedIn: Sandy RyzaBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today's episode, Ben and Michael dive deep into the intricacies of software development, innovation, and team dynamics. This episode explores the critical balance between building in-house tools versus leveraging open-source solutions, with real-world examples from Databricks.They discuss the creation and eventual abandonment of a benchmarking tool for warehouses and discuss the importance of evaluating user demand, effort, and impact before committing to development. They emphasize the role of empathy, constructive feedback, and team collaboration in driving successful projects. They share strategies to influence behavior within organizations, the significance of a blame-free culture, and the art of leading difficult conversations with stakeholders.From detailed discussions on customer feedback loops to practical advice on automating mundane tasks, this episode is packed with insights that will help you navigate the complex landscape of software development. So sit back, relax, and join us for a thoughtful and engaging conversation on how to turn challenges into opportunities for growth and innovation.SocialsLinkedIn: Michael berkLinkedIn: Benjamin WilsonBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today's episode, Ben and Michael dive deep into the intersection of education, AI, and innovative instructional design. Luis Garcia who is the President of PETE, delves into automating instructional design, content development, and assessments, shedding light on the evolving educational landscape and the pivotal role of evaluation and learning. Ben shares invaluable insights on leveraging chat GPT and generative AI to streamline documentation creation and evaluate knowledge, drastically cutting down processing times.Together, Luis and Ben discuss the positive reception and transformative potential of AI-driven micro-courses, text-to-speech features, and customized training tools in education. They also touch on the intense training involved in fields like nuclear reactor operation and the need for effective onboarding processes. Michael contributes by emphasizing empathy and strategic pacing in international business projects, while also summarizing instructional strategies and organizational tips for rapid learning and growth.Join them as they explore the crucial role of innovative AI technologies and personalized learning tools in reshaping education and business training, featuring insights from top industry professionals and thought leaders. And don't miss the chance to learn more about Pete and Collectiva. Get ready for a compelling discussion about enhancing learning outcomes and the future of education with AI!SocialsLinkedIn: Luis E. GarciaBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today's episode, our hosts Michael, Ben, and special guest Keith Goode delve deep into the transformative role of AI and machine learning in modern HR practices. They tackle a range of topics, starting with the innovative use of AI to streamline surveying and sentiment analysis in employee evaluations. They explore the exciting potential of AI models in technical data collection, particularly for interviews, and discuss how these models can assess candidates' sentiment and confidence levels, providing valuable insights into their fit for specific roles.They also hear about the emerging trends discussed at the recent Databricks Data and AI Summit, where generative AI for resume screening took center stage. They debate the challenges and opportunities of leveraging AI to reduce information overload in analytics, particularly within the complex hiring process. They emphasize the importance of explainable AI models, consulting scalability, and the perennial issue of data cleansing in HR.Additionally, the episode touches on the critical aspects of diversity and inclusion in the workplace, the influence of new legislation on workforce diversity modeling, and how companies can configure HR systems to suit their unique needs. They share insights into using advanced tools like XGBoost for predictive modeling, highlight the significance of face-to-face interactions in interview processes, and caution against over-reliance on automated resume screening.Join them as they navigate these thought-provoking discussions and more, shedding light on the intersection of AI, machine learning, and human resources. SocialsLinkedIn: Keith GoodeBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today's episode, they delve deep into the intertwining worlds of technology, security, and innovation with Aaron Painter, CEO at Nametag.Aaron kicks things off by underlining the cultural facets in hiring, emphasizing the virtues of being good listeners, intellectually curious, kind, and respectful while achieving tangible results. We also explore the collaborative spirit in group product planning and the pivotal role of diverse perspectives.From there, Ben takes us into the fascinating—and somewhat unnerving—advancements in deep fakes, particularly in image generation, and their implications for security and entertainment. This discussion also touches on the complexities of preventing deep fake attacks and the critical role of technology in mitigating these threats.Michael weighs in on how physical devices and user verification limit fraudulent deep fake activities, while Aaron offers invaluable advice on latching onto growing fields like AI for future-proofing your career. We also delve into a riveting recount of Ben’s early data science days, offering a glimpse into the tech evolution from Hadoop to cloud computing.Our conversation spans intriguing analogies, from the oil industry to AI, and examines the crucial shift toward cloud technologies, underpinned by end-use cases and consumer demands. We discuss the pressing need for secure identity verification in the digital age, exploring multifactor authentication and the delicate balance between security and user experience. Additionally, the episode covers Microsoft’s impact on global economies, with Aaron sharing heartfelt insights from his illustrious career.Join them as they navigate these compelling topics and more, offering a wealth of knowledge for developers, tech enthusiasts, and anyone keen on the future of technology. Tune in and prepare to elevate your understanding as we unfold the latest in machine learning, AI, and technological innovation.SocialsLinkedIn: Aaron PainterBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today’s episode, they dive into the intricate world of MLOps with Brad Micklea, a seasoned expert with extensive experience in software infrastructure and leadership roles at Eclipse Shay, Red Hat, AWS, and Jozu. Brad shares his journey of founding Jozu, an MLOps company that stands out with its commitment to open standards such as the OCI standard for packaging AI projects. Alongside Jozu, they explore KitOps, an innovative open-source project that simplifies version control and collaboration for AI teams.Join them as they discuss the challenges in integrating AI models into production, the importance of monitoring API usage, and the critical role of automated rollback systems in maintaining operational excellence. They also touch on the cultural differences in operational approaches between giants like AWS and Red Hat and hear first-hand experiences on the significance of transparency, trust, and efficient risk management in both startups and established companies.Whether you're a DevOps professional, MLOps practitioner, or data scientist transitioning to production, this episode is packed with valuable insights and practical advice to help you navigate the complexities of AI project management. Tune in to discover how Brad and his team are tackling these challenges head-on and learn how to set up your projects for success from the ground up!SocialsLinkedIn: Brad Micklea Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today's episode, they dive deep into the evolving landscape of software development. Join us as Kirk, the CTO and founder at Graphlit, shares his journey from traditional software at Microsoft to pioneering perception ML for drone-based aerial intelligence.  They explore the paradigm shift from object-oriented to functional programming, the crucial role of software architecture, and the challenges of maintaining consistent design and documentation in growing teams.They also get insights into Databricks' approach to user-friendly API design and the importance of learning management systems in knowledge distillation. Listen in as our speakers discuss the strategic decisions in scaling products, the nuances of open-source contributions, and the value of automation in modern development. Whether you're navigating a startup or a large enterprise, this episode is packed with expert advice on building robust, scalable systems and the dynamic decision-making needed to thrive in today's tech environment. Tune in and elevate your development game!SocialsLinkedIn: Kirk MarpleBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today's episode, Michael and Ben alongside our guest Alex Levin dive deep into the evolving landscape of AI development and its broader implications on business and society. You'll hear Ben emphasize reducing the cost and time of AI development by leveraging open-source models, while Alex draws parallels between the AI industry and flat-screen TVs, advocating for AI as a public good.The conversation traverses through the importance of compelling AI services, revenue-generating strategies, and the disruption AI brings—both in job creation and efficiency improvement. From personal anecdotes in semiconductor fabs to the pitfalls of the YC funding model, we explore various facets of success in the tech world. Alex brings a unique perspective from his background in psychology and entrepreneurship, touching on the importance of market timing, embracing uncertainty, and the significant role of mentorship.Whether you're a startup enthusiast or a seasoned tech veteran, this episode will provide invaluable insights on navigating the complexities of AI development, operational challenges for founders, and the essential balance between innovation and business strategy. So tune in, and let's get started on this journey through the cutting edge of technology with our insightful guests on Top End Devs!SocialsLinkedIn: Alex Levinalexrlevin.comBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today's episode, Michael and Ben dissect the process of building maintainable and impactful products, emphasizing the crucial balance between innovation and simplicity. They explore personal and group learning curves, the value of collaboration, and the indispensable role of peer review in creating robust solutions.They'll also touch upon the nuanced perspectives of working at top tech companies like Google and Databricks, examining how timing and project involvement can shape a developer's skillset and career trajectory. From the importance of understanding one's career goals to the powerful impact of a company's culture on code quality, they aim to uncover the multifaceted aspects of professional growth in tech.Join they as they delve into stories of overengineered solutions, the necessity of constructive feedback, and the collaborative efforts that define truly great products. Whether you're aspiring to join the elite 1% of developers, or simply looking to understand the dynamics of a high-functioning team, this episode is packed with insights and practical advice. So, tune in and let's explore the path to greatness together!Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

 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.They explore how technical skills, particularly in applied machine learning, are evaluated with a focus on their impact on business outcomes. Michael and Ben also address the common misalignments between job descriptions and the actual skills required, stressing the need for problem-solving capabilities and critical thinking over memorized knowledge.Additionally, they delve into the roles within data science—analysts, applied ML specialists, and researchers—highlighting the importance of fitting the right skills to the right job. They also touch on the evolving expectations and frustrations with the current hiring process, offering insights on how it can be improved.Stay tuned as they unpack these topics and more, including valuable tips for showcasing your skills effectively on resumes, and the significance of asking insightful questions during interviews. Whether you’re an aspiring data scientist or a seasoned professional, this episode is packed with practical advice and industry insights you won’t want to miss!SocialsLinkedIn: Ben WilsonLinkedIn: Michael BerkBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.