GPU acceleration is transforming how data scientists tackle computationally intensive problems in the AI and materials science fields. When dealing with billions of potential molecular combinations or massive datasets requiring dimensionality reduction, traditional CPU approaches often become prohibitively slow and expensive. How can data professionals determine when GPU acceleration will provide meaningful benefits to their workflows? Understanding the right applications for this technology can mean the difference between waiting hours versus minutes for critical results.

Nick Becker is a Group Product Manager at NVIDIA, focused on building RAPIDS and the broader accelerated data science ecosystem. Nick has a professional background in technology and government. Prior to NVIDIA, he worked at Enigma Technologies, a data science startup. Before Enigma, he conducted economics research and forecasting at the Federal Reserve Board of Governors, the central bank of the United States.

Dan Hannah is an Associate Director at SES AI Corporation. At SES, Dan leads a research program focused on discovering new battery materials using machine learning, chemical informatics, and physics-driven simulations. Prior to joining SES, Dan spent several years as a data scientist in the cybersecurity industry. Dan holds a Ph.D. in Physical Chemistry from Northwestern University and did a postdoctoral fellowship at Berkeley National Lab, where his focus was the discovery of novel inorganic materials for energy applications.

In the episode, Richie, Nick, and Dan explore the quest for new battery technologies, the role of data science and machine learning in material discovery, the integration of NVIDIA's GPU technology, the balance between computational simulations and lab work, and much more.

Links Mentioned in the Show:

• NVIDIA RAPIDS
• SES AI Corporation
• Connect with Dan and Nick
• Career Track: Machine Learning Scientist in Python
• Related Episode: Data Science Trends from 2 Kaggle Grandmasters with Jean-Francois Puget, Distinguished Engineer at NVIDIA & Chris Deotte, Senior Data Scientist at NVIDIA
• Rewatch sessions from RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

AI maturity isn't achieved through technology alone—it requires organizational alignment, cultural readiness, and strategic implementation. Companies across industries are working to move beyond experimental AI use toward systematic integration that delivers measurable business value. How do you assess where your organization stands on the AI maturity spectrum? What frameworks can help prioritize your efforts?

Eryn Peters, Co-founder & co-creator at AI Maturity Index, is a future of work evangelist. She is the co-creator of a tool for assessing AI maturity, and regularly advises companies on how to assess and improve their AI maturity. Eryn is also the Editor of the Weekly Workforce newsletter and the Principal at the Startup Consortium consultancy. Previously, she was the Global Director of the Association for the Future of Work, and VP of Marketing at Andela.

Iwo Szapar is a serial entrepreneur with a passion for creating impactful solutions that enable people to work smarter, not harder. He is the co-founder of several innovative initiatives, including Remote-how, Remote-First Institute, AI-Mentor, and the Saudi AI Leadership Forum. Throughout his career, Iwo has helped transform how over 3,000 companies—including Microsoft, Walmart, and ING Bank—approach the future of work.

In the episode, Richie, Eryn, and Iwo explore AI maturity in organizations, the balance between top-down and bottom-up AI adoption, the relationship between data and AI maturity, the importance of change management, practical steps for AI implementation, and much more.

Links Mentioned in the Show:

• AI Maturity Index
• Eryn’s Website
• Iwo’s Book: Remote Work Is The Way
• Connect with Eryn and Iwo
• State of Data & AI Literacy Report 2025
• Eryn’s previous webinar: Assessing Your Organization's AI Maturity
• Related Episode: Scaling Responsible AI Literacy with Uthman Ali, Global Head of Responsible AI at BP
• Rewatch sessions from RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

AI tooling continues to expand with specialized solutions for every step of the development process. For data scientists and engineers, this creates a paradox: more options but potentially more complexity and integration challenges. How do you determine which tools actually improve productivity versus adding unnecessary overhead? Should you prioritize flexibility with individual best-of-breed components or streamline with integrated platforms? What's the most effective way to bridge the gap between experimentation and production-ready AI applications?

William Falcon is an AI researcher and the CEO of Lightning AI. He is the creator of PyTorch Lightning, a lightweight framework designed for training models of any size. As the founder of Lightning AI, he leads the development of Lightning AI Studios and the AI Hub. Falcon also shares his expertise in AI research and machine learning engineering through educational content on YouTube and X (formerly Twitter). He is passionate about leveraging AI for social impact.

In the episode, Richie and William explore the NY AI hub, the journey from AI idea to production, diverse perspectives in AI development, how Lightning AI simplifies AI workflows, the significance of open-source models, and much more.

Links Mentioned in the Show:

• Lightning AI
• PyTorch Lightning
• Connect with William
• Course: Introduction to Deep Learning in PyTorch Course
• Related Episode: Building Multi-Modal AI Applications with Russ d'Sa, CEO & Co-founder of LiveKit
• Rewatch sessions from RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

The like button has transformed how we interact online, becoming a cornerstone of digital engagement with over 7 billion clicks daily. What started as a simple user interface solution has evolved into a powerful data collection tool that companies use to understand customer preferences, predict trends, and build sophisticated recommendation systems. The data behind these interactions forms what experts call the 'like graph' - a valuable network of connections that might be one of your company's most underutilized assets.

Bob Goodson is President and Founder of Quid, a Silicon Valley–based company whose AI models are used by a third of the Fortune 50. Before starting Quid, he was the first employee at Yelp, where he played a role in the genesis of the like button and observed firsthand the rise of the social media industry. After Quid received an award in 2016 from the World Economic Forum for “Contributions to the Future of the Internet,” Bob served a two-year term on WEF’s Global Future Council for Artificial Intelligence & Robotics. While at Oxford University doing graduate research in language theory, Bob co-founded Oxford Entrepreneurs to connect scientists with business-minded students. Bob is co-author of a new book, Like: The Button That Changed the World, focussed on the origins of the ubiquitous Like Button in social media.

In the episode, Richie and Bob explore the origins of the like button, its impact on user interaction and business, the evolution of social media features, the significance of relational data, and the future of social networks in the age of AI, and much more.

Links Mentioned in the Show:

• Bob’s book—Like: The Button That Changed the World
• Connect with Bob
• Course: Analyzing Social Media Data in Python
• Related Episode: How I Nearly Got Fired For Running An A/B Test with Vanessa Larco, Former Partner at New Enterprise Associates
• Rewatch sessions from RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

Welcome to DataFramed Industry Roundups! In this series of episodes, Adel & Richie sit down to discuss the latest and greatest in data & AI. In this episode, we touch upon the launch of OpenAI’s O3 and O4-mini models, Meta’s rocky release of Llama 4, Google’s new agent tooling ecosystem, the growing arms race in AI, the latest from the Stanford AI Index report, the plausibility of AGI and superintelligence, how agents might evolve in the enterprise, global attitudes toward AI, and a deep dive into the speculative—but chilling—AI 2027 scenario. All that, Easter rave plans, and much more.

Links Mentioned in the Show:

• Introducing OpenAI o3 and o4-mini
• The Median: Scaling Models or Scaling People? Llama 4, A2A, and the State of AI in 2025
• LLama 4
• Google: Announcing the Agent2Agent Protocol (A2A)
• Stanford University's Human Centered AI Institute Releases 2025 AI Index Report
• AI 2027
• Rewatch sessions from RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

The roles within AI engineering are as diverse as the challenges they tackle. From integrating models into larger systems to ensuring data quality, the day-to-day work of AI professionals is anything but routine. How do you navigate the complexities of deploying AI applications? What are the key steps from prototype to production? For those looking to refine their processes, understanding the full lifecycle of AI development is essential. Let's delve into the intricacies of AI engineering and the strategies that lead to successful implementation.

Maxime Labonne is a Senior Staff Machine Learning Scientist at Liquid AI, serving as the head of post-training. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI/ML. An active blogger, he has made significant contributions to the open-source community, including the LLM Course on GitHub, tools such as LLM AutoEval, and several state-of-the-art models like NeuralBeagle and Phixtral. He is the author of the best-selling book “Hands-On Graph Neural Networks Using Python,” published by Packt.

Paul-Emil Iusztin designs and implements modular, scalable, and production-ready ML systems for startups worldwide. He has extensive experience putting AI and generative AI into production. Previously, Paul was a Senior Machine Learning Engineer at Metaphysic.ai and a Machine Learning Lead at Core.ai. He is a co-author of The LLM Engineer's Handbook, a best seller in the GenAI space.

In the episode, Richie, Maxime, and Paul explore misconceptions in AI application development, the intricacies of fine-tuning versus few-shot prompting, the limitations of current frameworks, the roles of AI engineers, the importance of planning and evaluation, the challenges of deployment, and the future of AI integration, and much more.

Links Mentioned in the Show:

• Maxime’s LLM Course on HuggingFace
• Maxime and Paul’s Code Alongs on DataCamp
• Decoding ML on Substack
• Connect with Maxime and Paul
• Skill Track: AI Fundamentals
• Related Episode: Building Multi-Modal AI Applications with Russ d'Sa, CEO & Co-founder of LiveKit
• Rewatch sessions from RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

Data-driven turnarounds are transforming how struggling businesses find their path back to profitability. When companies falter, the key to recovery can often lies in understanding which 20% of customers and products generate 80% of profits. But how do you quickly identify these critical assets when time is running out? What metrics should you prioritize when cash flow is tight? For data professionals, the challenge extends beyond analysis to implementation—balancing the need for automation of routine tasks while reskilling employees for higher-value work. The intersection of empathy and analytics becomes crucial as teams navigate the emotional journey of organizational change while making tough decisions based on hard numbers.

Bill Canady is CEO at Arrowhead Engineered Products and a global business executive with over 30 years of experience across a range of industries. Bill is known for aligning with stakeholders to establish clear, growth-oriented strategies, as well as leading global public, private, and private equity-owned companies by building strong leadership teams and fostering deep relationships. As the former CEO of OTC Industrial Technologies, he oversaw $1 billion in annual sales. Under his leadership, OTC achieved over 43% revenue growth and a 78% increase in earnings. Throughout his career, Bill has guided organizations through complex challenges in regulatory, investor, and media landscapes. Drawing on his extensive experience, he developed the Profitable Growth Operating System (PGOS) to help business leaders worldwide drive sustainable, profitable growth.

In the episode, Richie and Bill explore the journey from panic to profit in failing companies, the 100-day turnaround process, leveraging data for decision-making, the Pareto principle in business, automation's role in efficiency, and the importance of empathy and continuous learning in leadership, and much more.

Links Mentioned in the Show:

• Bill’s new book: From Panic to Profit
• The 80/20 CEO by Bill Canady
• Connect with Bill
• Bill’s website
• Skill Track: AI Leadership
• Related Episode: Leadership in the AI Era with Dana Maor, Senior Partner at McKinsey & Company
• Sign up to attend RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

We live in an era where data is abundant, yet making sense of it is harder than ever. The best insights often go unnoticed—not because they lack value, but because they lack a compelling story. Simply presenting numbers isn’t enough; the way we shape and frame data determines whether it sparks action or fades into the background. Crafting a strong data story means knowing your audience, structuring your insights around a clear problem, goal, action, and impact, and ensuring your narrative is not just persuasive, but ethical. So how do we bridge the gap between information and understanding? How can we tailor data stories to resonate with decision-makers, stakeholders, and the public in ways that drive meaningful change?

Kat Greenbrook is a Data Storyteller from Aotearoa, New Zealand. She is a consultant, workshop facilitator, industry speaker, and founder of the data storytelling company Rogue Penguin Ltd. With a unique blend of science, business, and design, she empowers data professionals to communicate data effectively through storytelling. Kat’s book, The Data Storyteller's Handbook, is the result of hundreds of data storytelling workshops, along with years of refining content and techniques. It represents the very best of what she has learned and witnessed.

In the episode, Richie and Kat explore the art of data storytelling, the importance of audience-tailored narratives, the problem-goal-action-impact framework, ethical storytelling, and much more.

Links Mentioned in the Show:

• Kat’s Book: The Data Storyteller's Handbook
• Connect with Kat
• Course: Data Storytelling Concepts
• Related Episode: Data Storytelling and Visualization with Lea Pica from Present Beyond Measure
• Rewatch sessions from RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

Misconceptions about AI's capabilities and the role of data are everywhere. Many believe AI is a singular, all-knowing entity, when in reality, it's a collection of algorithms producing intelligence-like outputs. Navigating and understanding the history and evolution of AI, from its origins to today's advanced language models is crucial. How do these developments, and misconceptions, impact your daily work? Are you leveraging the right tools for your needs, or are you caught up in the allure of cutting-edge technology without considering its practical application?

Andriy Burkov is the author of three widely recognized books, The Hundred-Page Machine Learning Book, The Machine Learning Engineering Book, and recently The Hundred-Page Language Models book. His books have been translated into a dozen languages and are used as textbooks in many universities worldwide. His work has impacted millions of machine learning practitioners and researchers. He holds a Ph.D. in Artificial Intelligence and is a recognized expert in machine learning and natural language processing. As a machine learning expert and leader, Andriy has successfully led dozens of production-grade AI projects in different business domains at Fujitsu and Gartner. Andriy is currently Machine Learning Lead at TalentNeuron.

In the episode, Richie and Andriy explore misconceptions about AI, the evolution of AI from the 1950s, the relevance of 20th-century AI research, the role of linear algebra in AI, the resurgence of recurrent neural networks, advancements in large language model architectures, the significance of reinforcement learning, the reality of AI agents, and much more.

Links Mentioned in the Show:

• Andriy’s books: The Hundred-page Machine Learning Book, The Hundred-page Language Models Book
• TalentNeuron
• Connect with Andriy
• Skill Track: AI Fundamentals
• Related Episode: Unlocking Humanity in the Age of AI with Faisal Hoque, Founder and CEO of SHADOKA
• Rewatch sessions from RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

The explosion of content in market research has created a paradox - more information but less time to consume it. Companies are now turning to AI chatbots to solve this problem, transforming how professionals interact with research data. Instead of expecting teams to read everything, these tools allow users to extract precisely what they need when they need it. This approach is proving not just more efficient but actually increases engagement with underlying content. How might your organization benefit from more targeted access to insights? What valuable information might be buried in your existing research that AI could help surface?

With over 30 years of experience in marketing, media, and technology, Dan Coates is the President and co-founder of YPulse, the leading authority on Gen Z and Millennials. YPulse helps brands like Apple, Netflix, and Xbox understand and communicate with consumers aged 13–39, using data and insights from over 400,000 interviews conducted annually across seven countries. Prior to founding YPulse, Dan co-founded SurveyU, an online community and insights platform targeting youth, which merged with YPulse in 2009. He also led the introduction of Globalpark’s SAAS platform into the North American market, until its acquisition by QuestBack in 2011. In addition, Dan has held senior roles at Polimetrix, SPSS, PlanetFeedback, and Burke, where he developed cutting-edge practices and products for online marketing insights and transitioned several ventures from early stages to high-value acquisitions.

In the episode, Richie and Dan explore the creation of an AI chatbot for market research, addressing customer engagement challenges, the integration of AI in content consumption, the impact of AI on business strategies, and the future of AI in market research, and much more.

Links Mentioned in the Show:

• YPulse
• Connect with Dan
• Haystack by Deepset
• Unmanaged: Master the Magic of Creating Empowered and Happy Organizations by Jack Skeels
• Skill Track: AI Fundamentals
• Related Episode: Can You Use AI-Driven Pricing Ethically? with Jose Mendoza, Academic Director & Clinical Associate Professor at NYU
• Rewatch sessions from RADAR: Skills Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business