Today, join Michael and Ben as they delve into crucial topics surrounding code security and the safe execution of machine learning models. This episode focuses on preventing accidental key leaks in notebooks, creating secure environments for code execution, and the pros and cons of various isolation methods like VMs, containers, and micro VMs.They explore the challenges of evaluating and executing generated code, highlighting the risks of running arbitrary Python code and the importance of secure evaluation processes. Ben shares his experiences and best practices, emphasizing human evaluation and secure virtual environments to mitigate risks.The episode also includes an in-depth discussion on developing new projects with a focus on proper engineering procedures, and the sophisticated efforts behind Databricks' Genie service and MLflow's RunLLM. Finally, Ben and Michael explore the potential of fine-tuning machine learning models, creating high-quality datasets, and the complexities of managing code execution with AI.Tune in for all this and more as we navigate the secure pathways to responsible and effective machine learning development.SocialsLinkedIn: Michael BerkLinkedIn: Ben WilsonBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

They delve into the journeys and insights of distinguished leaders in the development world. In today's episode, Michael engages with  Brian Vallelunga, the visionary CEO of Doppler. Brian shares his compelling journey from early tech innovations to leading multiple startups and eventually founding Doppler, a centralized cloud secret management tool.Brian emphasizes the importance of making security tools enticing for developers, comparing it to making vegetables taste like candy, to boost productivity. His team’s strategy revolves around seamless integration into developers’ workflows, featuring a VS Code extension and automatic syncing akin to Dropbox, enhancing efficiency and ease of use.They explore Doppler's competitive edge and how it partners with major cloud resource managers, making two-click integrations effortless. Brian also discusses their customer-centric development approach and the release of enterprise features like two-person approval and config inheritance, designed for complex organizational needs.Brian's entrepreneurial journey is marked by significant pivots driven by frustration and market demand, rather than strategic planning alone. He shares candid thoughts on the impact of founders and success, cautioning against the allure of celebrity status and emphasizing team contributions.Join them as they dive into insightful discussions on building developer-friendly security tools, the nuances of secret management, and Brian's perspectives on startup growth and innovation. Discover how Doppler is revolutionizing secret management, one integration at a time.SocialsLinkedIn: Brian VallelungaBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today's episode, Michael and Ben discuss peer review, specifically Michael's experiences. Michael explains his unconventional path, starting with advanced math as a child, then struggling with a math-heavy computer science program in college. He pivoted to environmental studies, focusing on side projects and extracurriculars. These projects led to his first job, and later to a role at a boxing streaming service (2B) with a rigorous peer review process. Ben asks about the importance of the peer review process, and Michael highlights its value in catching errors and ensuring code quality, especially when working under pressure. Moreover, Ben discusses the learning experience at different career stages, noting that junior developers learn from senior developers' code and feedback. Ben discusses the differences in peer review for different types of code changes. They discuss the importance of thorough review for critical code changes and many more!SocialsLinkedIn: Ben WilsonLinkedIn: Michael Berk Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

 In today's episode, Ben and Michael discuss how to handle situations involving individuals lacking expertise in machine learning projects. They explore scenarios where a team lacks expertise, considering approaches for consultants or team members. They discuss various personality types encountered in such situations, including those overly suspicious or resistant to change. Moreover, they discuss how to convince a boss that a proposed project is a bad idea, suggesting a structured approach with clear estimates, risk assessment, and alternative solutions. They emphasize the importance of honesty, transparency, and presenting options with clear pros and cons. The discussion then returns to the Gen AI time-series case study, suggesting a presentation of multiple options, including established algorithms and the Gen AI approach, to facilitate a data-driven decision.Finally, the episode addresses the scenario of a teammate being untrained about a system they built, suggesting a combination of direct but constructive feedback and a collaborative approach to identify the root cause of the issue.SocialsLinkedIn Ben WilsonLinkedIn Michael BerkBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In this episode, Michael and Ben dive deep into the intersection of education and technology with their insightful guest, Daniel Hiterer.Michael, a data engineering and machine learning expert, and Ben, an integrator of Gen AI tools, navigate through Danny's unique perspective on the impact of nurturing educational environments. Currently working at Cornell’s Studio entrepreneurship program, Danny brings a multidisciplinary background, combining history and instructional technology, and shares his vision for the future of learning.This episode explores the transformative power of nurture in education, the evolving role of Gen AI in fostering curiosity, and the challenges and opportunities in integrating AI into the learning process. Danny provides thought-provoking insights on emotional access points, curiosity-driven learning, and the delicate balance between educational goals and productivity tools.Listen in as they discuss personalized education, the promise of AI-assisted learning, and the future trajectory of superintelligence in education. Plus, hear personal anecdotes from Ben and Michael about their own learning journeys and the evolving landscape of curiosity and knowledge.SocialsLinkedIn: Daniel HitererBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Today, they dive deep into the fascinating intersection of open-source development and machine learning.  Michael and Ben are joined by distinguished guest, Görkem Erkan, CTO and seasoned engineer at Jozu.Görkem shares his illustrious career journey from Nokia to Red Hat, his contributions to the Eclipse Foundation, and his current focus on MLOps. They explore his passion for open-source projects, the cultural and communication impacts on software design, and the unique challenges posed by integrating open-source frameworks with proprietary systems. Ben provides critical insights on the complexities of managing scalable backend services and the hurdles in translating SaaS offerings to open-source platforms.Tune in to learn about the innovative practices at Jozu, the role of open communication in team success, and the nuanced debate on maintaining separate proprietary and open-source codebases. This episode is packed with valuable lessons for developers, tech leaders, and anyone interested in the future of machine learning and open-source development. SocialsLinkedIn: Görkem Ercan Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In this week's episode, Michael and Ben sit down with Artem Koren, Chief Product Officer at Sembly AI, to explore the future of AI integration in the workplace. We'll delve into Sembly AI's mission to accelerate team efficiency through powerful AI tools—imagine an Iron Man suit for your daily tasks. From proactive AI assisting with time-consuming tasks to ethical considerations in data privacy, this episode covers the cutting-edge developments and challenges in AI implementation.They also discuss the evolving landscape of workplace automation, the intricacies of data collection, and the balance between privacy and productivity. They also highlight Sembly's latest advancements like Semblian 2.0, a breakthrough in digital twin technology that promises to redefine meeting productivity. Join them for an in-depth conversation on AI's transformative potential, the ethical responsibilities it entails, and the practical impacts on the project. LinksSemblian 2.0SocialsLinkedIn: Artem KorenBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

In today's episode, Michael is joined by Hikari Senju the Founder and CEO at Omneky. He starts by discussing how he built Omneky, an AI-Driven Marketing Platform. They dive into Hikari's approach to working with customers on brand strategy and content. They also talk about the increasing importance of brands in a digital, AI-driven world. Additionally, they tackle Hikari's perspective on how generative AI will impact the advertising industry. Tune in on how ML is Reshaping The Advertising Industry.SocialsLinkedIn: Hikari SenjuBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Today, Ben and Michael dive into a compelling discussion on the intricate dance between challenges, feedback, mentorship, and growth in the field of software development. In this episode, Michael shares their journey of overcoming the pains of independent problem-solving before receiving effective guidance. As we explore their experiences with Ben, they uncover the vital importance of openness to feedback and the profound value of peer review in refining solutions.They delve into technical aspects, including Python's Pytest framework for unit tests and the delicate balance between complexity and simplicity in testing for maintainability and readability. Additionally, they touch on Michael's hands-on learning curve, tackling unfamiliar concepts such as RAG, embeddings, LLMs, and Git development, all while managing significant time constraints and social commitments.Moreover, Ben shares his mentorship philosophy, likening it to military training—pushing mentees to their limits without prior warning to foster resilience and self-improvement. They also discuss the importance of documentation, bug bashes, and the fine art of balancing integration and unit tests to ensure robust and thorough software.Join them as they explore the journey from initial struggle to increased autonomy and confidence, using real-world examples of testing gaps, code complexities, and the powerful impact of daily feedback. Whether you're a seasoned developer or just starting your tech career, this episode is packed with valuable insights to enhance your learning and development process. So, stay tuned and dive right in!Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Michael Berk dives deep into the adventures of AI and machine learning with our special guest, Richmond Alake, a staff developer advocate at MongoDB. Richmond's journey from web development to AI was driven by a quest for excitement and new challenges. In this episode, he shares how he transitioned into the AI field, his passion for using writing as a learning tool, and the importance of continuous learning in evolving tech landscapes.They explore the intricacies of building and evaluating Retrieval-Augmented Generation (RAG) systems, the benefits of MongoDB's versatile database functionalities, and the pressing challenges in machine learning data collection and evaluation. Richmond also gives us a peek into MongoDB's advanced solutions for AI application development and how strategic data chunking can impact efficiency.Whether you're a budding AI enthusiast or an experienced developer looking to expand your horizons, this episode is packed with practical advice, career insights, and the latest trends in AI and machine learning. Stay tuned as we uncover how to navigate the complexity of RAG pipelines and the evolving landscape of generative AI. Let's get started!SocialsLinkedIn: Richmond AlakeBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

Today, we have a special guest Abi Aryan, an accomplished founder of Abide AI and a seasoned expert in machine learning. Joining us are your hosts, Michael Berk and Ben Wilson, who bring a wealth of experience from Databricks.In this episode, Ben shares his journey navigating the intricacies of deep learning and the surprising effectiveness of simpler solutions over complex algorithms. Abi lends her insights to the balancing act between innovation and practicality in tech adoption, influenced by career stability and venture capital demands. They also explore Abi's passion for recommender systems and audio speech synthesis, and the potential these fields hold for e-commerce and inclusivity.Abi also gives us a glimpse into her research methodology, her approach to autonomous agents, and the challenges she faced with bias and imposter syndrome. As they dissect consulting strategies, experiment design, and the art of fostering a collaborative environment, this episode is packed with valuable lessons for any tech enthusiast.So, get ready to tune in, take notes, and be inspired by the fascinating stories and insights from our expert guest and hosts.SocialsAbi AryanLinkedIn: Abi AryanBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

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.

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.SocialsLinkedIn: Brooke WenigBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

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.They explore AI's potential to monitor, defend, and even augment human efforts against security threats, touching on both the opportunities and ethical challenges. They also examine AI's role in protecting against social media scams and phishing attacks, envisioning a future where AI acts as our digital guardian.Whether you're in cybersecurity, development, or simply curious about AI's impact on security, this episode is packed with valuable insights. Stay tuned for a fascinating discussion!SocialsLinkedIn: Daniel MiesslerBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

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.SocialsLinkedIn: Fernando LopezBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

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

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

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.

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.

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.

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.

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.

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.

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.

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.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome 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.

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.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipLinks Dlib.netSocialsLinkedIn: Davis KingAdvertising 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.

Ben and Michael delve into the crucial aspects of coding, culture, and collaboration. From the importance of proper formatting and consistency in Python code to the challenges of changing organizational culture, they explore the impact of code quality on team dynamics and project success. They emphasize empathy, communication, and the power of a positive vision to drive change. Tune in to gain insights on tackling diverse problems, the role of documentation, and the significance of modularization in codebases. Join them as they navigate the world of development and seek to create a positive work environment where clear, understandable code thrives.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome 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.

Konstantin Gizdarski and Jonas Timmermann are software engineers at Lyft. They dive deep into the world of machine learning and engineering at Lyft. Join them as they explore the challenges and successes of implementing reinforcement learning, contextual bandits, and advanced AI technologies in a real-world business environment. Learn about the collaborative engineering culture at Lyft, the development of new ML capabilities, and the unique approaches to infrastructure and model deployment. Listen in as industry experts share their insights on accelerating decision-making processes, simplifying tools for end users, and finding innovative solutions to common engineering challenges.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Konstantin GizdarskiLinkedIn: Jonas TimmermannAdvertising 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.

James Lamb is a senior software engineer at NVIDIA. They delve into the world of open-source contributions and the impact of traditional machine learning on the modern economy. James shares his journey of becoming a maintainer of renowned open-source projects while offering valuable insights into the benefits and motivations behind contributing to the community.Join them as they explore the significance of human review in the PR process, the value of automated feedback, and the importance of maintaining a positive and inclusive environment for contributors in open-source projects.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: James LambTwitter: @_jameslambAdvertising 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.

 Richard Berk delves into the exciting world of machine learning in a thought-provoking discussion on a wide range of topics. They explore the potential for Westworld-style androids, considerations in the criminal justice system, ethical implications of AI in warfare, and the challenges of understanding uncertainty in real-world data. From advanced language models and genetic algorithms to the impact of AI on everyday life, get ready for a fascinating and insightful conversation that will expand your understanding of the evolving landscape of machine learning.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome 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.

Michael and Ben take a deep dive into the insightful journey spanning nuclear engineering, software development, and mentorship at Databricks. They delve into the complexities of career progression, the importance of humility and honesty in mentoring relationships, and the value of fostering a collaborative engineering culture. Join us as we explore the pivotal moments and impactful lessons that have shaped Ben's career and influenced their perspective on the ever-evolving tech industry.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome 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.

Piotr Skalski is a computer vision engineer at Roboflow. They dive deep into the world of computer vision and AI technology, exploring the utilization of video, photo, and imagery data in array processing and NLP. They discuss the challenges and opportunities presented by these fields, as well as the impact of AI technology on various industries, including civil engineering and manufacturing. Join us as we explore the fascinating intersection of technology, innovation, and the future of software development.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Piotr SkalskiAdvertising 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.

 In today's episode, we speak with Neil Theise, a pathologist at NYU and author of Notes on Complexity: A Scientific Theory of Connection, Consciousness and Being. Expect to learn about complexity theory and its implications for sentience, how great ideas are formed, whether AGI can be built with silicon-based computers, and much more!SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Neil TheiseAdvertising 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.

Get the Black Friday/Cyber Monday "Double Your Productivity by 5pm Today" DealCoupon Code: "THRIVE" for a GIANT discountAre you looking at all the layoffs and uncertainty going on and wondering if your company is the next to cut back? Or, maybe you're a freelancer or entrepreneur who is trying to figure out how to deliver more value to gain or retain customers?Mani Vaya joins Charles Max Wood to discuss the one thing that both of them use to more than double their productivity on a daily basis.Mani has read 1,000's of productivity books over the last several years and has formulated a methodology for getting more done, but found that he lacked the discipline to follow through on his plans.The he found the one thing that kept him on track and made him so productive that he is now getting all of his work done and was able to live the life he wants.Chuck also weighs in on how Mani's technique has worked for him and allows him to spend more time with his wife and kids, run a podcast network, and a nearly full time contract.Join the episode to learn how Chuck and Mani get into a regular flow state with their work and consistently deliver at work.Advertising 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.

In today's episode, we speak with Agata Checinska (Spotify) and Kasia Batko-Toluc (Citizen Network Watchdog Poland) about data privacy, accessibility, and accuracy. Expect to learn about how Poland approaches these sensitive topics, the power of deep fakes, and much more!SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Agata ChecinskaLinkedIn: Katarzyna Batko-TolucAdvertising 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.

In today's episode, we speak with Pierre Eliseeff, co-founder of Analyzr and causal inference expert. Expect to learn a 3-step blueprint for doing causal analysis, thinking critically about data, creating successful projects, and much more!SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome 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.

In today's episode, we chat with Sylvain Lesage from Hugging Face, a specialist in data visualization and software engineering. Dive in to discover insights about Hugging Face's software engineering environment, invaluable data visualization techniques, and more!SponsorsZilliz, who makes a vector database for the enterpriseChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Sylvain LesageAdvertising 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.

In today's episode, we chat with Adam Ross Nelson - a data scientist, career mentor, and writer. Our primary focus is understanding when you've achieved "good enough" skills. Additionally, we explore team dynamics, the importance of soft skills, and offer career guidance for budding data scientists.SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome a Top 1% Dev with a Top End Devs MembershipLinksData Science Career AcceleratorHow to Become a Data ScientistConfident Data ScienceAdvertising 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.

In today's episode, we delve into a popular topic at Databricks: building recommendation engines. We'll guide you on how to plan such projects, measure your model's success, suggest effective algorithms, and cover many other insights!SponsorsChuck's Resume TemplateDeveloper Book Club startingBecome 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.