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Machine learning and AI have become essential tools for delivering real-time solutions across industries. However, as these technologies scale, they bring their own set of challenges—complexity, data drift, latency, and the constant fight between innovation and reliability. How can we deploy models that not only enhance user experiences but also keep up with changing demands? And what does it take to ensure that these solutions are built to adapt, perform, and deliver value at scale?
Rachita Naik is a Machine Learning (ML) Engineer at Lyft, Inc., and a recent graduate of Columbia University in New York. With two years of professional experience, Rachita is dedicated to creating impactful software solutions that leverage the power of Artificial Intelligence (AI) to solve real-world problems. At Lyft, Rachita focuses on developing and deploying robust ML models to enhance the ride-hailing industry’s pickup time reliability. She thrives on the challenge of addressing ML use cases at scale in dynamic environments, which has provided her with a deep understanding of practical challenges and the expertise to overcome them. Throughout her academic and professional journey, Rachita has honed a diverse skill set in AI and software engineering and remains eager to learn about new technologies and techniques to improve the quality and effectiveness of her work.
In the episode, Adel and Rachita explore how machine learning is leveraged at Lyft, the primary use-cases of ML in ride-sharing, what goes into an ETA prediction pipeline, the challenges of building large scale ML systems, reinforcement learning for dynamic pricing, key skills for machine learning engineers, future trends across machine learning and generative AI and much more.
Links Mentioned in the Show:
Engineering at Lyft on MediumConnect with RachitaResearch Paper—A Better Match for Drivers and Riders: Reinforcement Learning at LyftCareer Track: Machine Learning EngineerRelated Episode: Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling AuthorSign up to RADAR: Forward EditionNew to DataCamp?
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As AI continually changes how businesses operate, new questions emerge around ethics and privacy. Nowadays, algorithms can set prices and personalize offers, but how do companies ensure they’re doing this responsibly? What does it mean to be transparent with customers about data use, and how can businesses avoid unintended bias? Balancing innovation with trust is key, but achieving this balance isn’t always straightforward.
Dr. Jose Mendoza is Academic Director and Clinical Associate Professor in Integrated Marketing at NYU, and was formerly an Associate Professor of Practice at The University of Arizona in Tucson, Arizona. His focus is on consumer pricing, digital retailing, intelligent retail stores, neuromarketing, big data, artificial intelligence, and machine learning. Previously, he taught marketing courses at Sacred Heart University and Western Michigan University. He is also an experienced senior global marketing executive with over 18 years of experience in global marketing alone and a career as an Engineer in Information Sciences. Dr. Mendoza is also a Doctoral Researcher in Strategic and Global pricing, Consumer Behavior, and Pricing Research methodologies. He had international roles in Latin America, Europe, and the USA with scope in over 50 countries.
In the episode, Richie and Jose explore AI-driven pricing, consumer perceptions and ethical pricing, the complexity of dynamic pricing models, explainable AI, data privacy and customer trust, legal and ethical guardrails, innovations in dynamic pricing and much more.
Links Mentioned in the Show:
NYUConnect with JoseAmazon Dynamic Pricing Strategy in 2024Course: AI EthicsRelated Episode: The Future of Marketing Analytics with Cory Munchbach, CEO at BlueConicSign up to RADAR: Forward EditionNew to DataCamp?
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With the recent rapid advancements in AI comes the challenge of navigating an ever-changing field of play, while ensuring the tech we use serves real-world needs. As AI becomes more ingrained in business and everyday life, how do we balance cutting-edge development with practicality and ethical responsibility? What steps are necessary to ensure AI’s growth benefits society, aligns with human values, and avoids potential risks? What similarities can we draw between the way we think, and the way AI thinks for us?
Terry Sejnowski is one of the most influential figures in computational neuroscience. At the Salk Institute for Biological Studies, he runs the Computational Neurobiology Laboratory, and hold the Francis Crick Chair. At the University of California, San Diego, he is a Distinguished Professor and runs a neurobiology lab. Terry is also the President of the Neural Information Processing (NIPS) Foundation, and an organizer of the NeurIPS AI conference. Alongside Geoff Hinton, Terry co-invented the Boltzmann machine technique for machine learning. He is the author of over 500 journal articles on neuroscience and AI, and the book "ChatGPT and the Future of AI".
In the episode, Richie and Terry explore the current state of AI, historical developments in AI, the NeurIPS conference, collaboration between AI and neuroscience, AI’s shift from academia to industry, large vs small LLMs, creativity in AI, AI ethics, autonomous AI, AI agents, superintelligence, and much more.
Links Mentioned in the Show:
NeurIPS ConferenceTerry’s Book—ChatGPT and the Future of AI: The Deep Language RevolutionConnect with TerryTerry on SubstackCourse: Data Communication ConceptsRelated Episode: Guardrails for the Future of AI with Viktor Mayer-Schönberger, Professor of Internet Governance and Regulation at the University of OxfordSign up to RADAR: Forward EditionNew to DataCamp?
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Generative AI and data are more interconnected than ever. If you want quality in your AI product, you need to be connected to a database with high quality data. But with so many database options and new AI tools emerging, how do you ensure you’re making the right choices for your organization? Whether it’s enhancing customer experiences or improving operational efficiency, understanding the role of your databases in powering AI is crucial.
Andi Gutmans is the General Manager and Vice President for Databases at Google. Andi’s focus is on building, managing, and scaling the most innovative database services to deliver the industry’s leading data platform for businesses. Prior to joining Google, Andi was VP Analytics at AWS running services such as Amazon Redshift. Prior to his tenure at AWS, Andi served as CEO and co-founder of Zend Technologies, the commercial backer of open-source PHP. Andi has over 20 years of experience as an open source contributor and leader. He co-authored open source PHP. He is an emeritus member of the Apache Software Foundation and served on the Eclipse Foundation’s board of directors. He holds a bachelor’s degree in computer science from the Technion, Israel Institute of Technology.
In the episode, Richie and Andi explore databases and their relationship with AI and GenAI, key features needed in databases for AI, GCP database services, AlloyDB, federated queries in Google Cloud, vector databases, graph databases, practical use cases of AI in databases and much more.
Links Mentioned in the Show:
GCPConnect with AndiAlloyDB for PostgreSQLCourse: Responsible AI Data ManagementRelated Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at PineconeSign up to RADAR: Forward EditionNew to DataCamp?
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Mastering the technical side of data and AI is one thing, but communicating those insights effectively is a whole different challenge. How do you make sure your data is understood, acted upon, and influences decisions? It’s not just about presenting the right numbers—it’s about framing them in a way that resonates with different audiences. But how do you tailor your communication to different stakeholders and ensure your message cuts through? What strategies can you use to make your insights truly impactful?
Wes Kao is an entrepreneur, marketer, coach, and advisor who writes at newsletter.weskao.com. She is co-founder of Maven, an edtech company that raised $25M from First Round and Andreessen Horowitz. Previously, she co-founded the altMBA with bestselling author Seth Godin.
In the episode, Richie and Wes explore communication skills, tailoring to your audience, persuasion vs information, feedback and behavioral change, intellectual honesty, judgement and analytical thinking, management and ownership, dealing with mistakes, conflict management, career advice for data practitioners and much more.
Links Mentioned in the Show:
Wes’ WebsiteConnect with Wes10,000 Hours Concept by Malcolm GladwellCourse: Data Communication ConceptsRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: Forward EditionNew to DataCamp?
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Building a robust data infrastructure is crucial for any organization looking to leverage AI and data-driven insights. But as your data ecosystem grows, so do the challenges of managing, securing, and scaling it. How do you ensure that your data infrastructure not only meets today’s needs but is also prepared for the rapid changes in technology tomorrow? What strategies can you adopt to keep your organization agile, while ensuring that your data investments continue to deliver value and support business goals?
Saad Siddiqui is a venture capitalist for Titanium Ventures. Titanium focus on enterprise technology investments, particularly focusing on next generation enterprise infrastructure and applications. In his career, Saad has deployed over $100M in venture capital in over a dozen companies. In previous roles as a corporate development executive, he has executed M&A transactions valued at over $7 billion in aggregate. Prior to Titanium Ventures he was in corporate development at Informatica and was a member of Cisco's venture investing and acquisitions team covering cloud, big data and virtualization.
In the episode, Richie and Saad explore the business impacts of data infrastructure, getting started with data infrastructure, the roles and teams you need to get started, scalability and future-proofing, implementation challenges, continuous education and flexibility, automation and modernization, trends in data infrastructure, and much more.
Links Mentioned in the Show:
Titanium VenturesConnect with SaadCourse - Artificial Intelligence (AI) StrategyRelated Episode: How are Businesses Really Using AI? With Tathagat Varma, Global TechOps Leader at Walmart Global TechRewatch sessions from RADAR: AI EditionNew to DataCamp?
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Businesses are collecting more data than ever before. But is bigger always better? Many companies are starting to question whether massive datasets and complex infrastructure are truly delivering results or just adding unnecessary costs and complications. How can you make sure your data strategy is aligned with your actual needs? What if focusing on smaller, more manageable datasets could improve your efficiency and save resources, all while delivering the same insights?
Ryan Boyd is the Co-Founder & VP, Marketing + DevRel at MotherDuck. Ryan started his career as a software engineer, but since has led DevRel teams for 15+ years at Google, Databricks and Neo4j, where he developed and executed numerous marketing and DevRel programs. Prior to MotherDuck, Ryan worked at Databricks and focussed the team on building an online community during the pandemic, helping to organize the content and experience for an online Data + AI Summit, establishing a regular cadence of video and blog content, launching the Databricks Beacons ambassador program, improving the time to an “aha” moment in the online trial and launching a University Alliance program to help professors teach the latest in data science, machine learning and data engineering.
In the episode, Richie and Ryan explore data growth and computation, the data 1%, the small data movement, data storage and usage, the shift to local and hybrid computing, modern data tools, the challenges of big data, transactional vs analytical databases, SQL language enhancements, simple and ergonomic data solutions and much more.
Links Mentioned in the Show:
MotherDuckThe Small Data ManifestoConnect with RyanSmall DataSF conferenceRelated Episode: Effective Data Engineering with Liya Aizenberg, Director of Data Engineering at AwayRewatch sessions from RADAR: AI EditionNew to DataCamp?
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Businesses are constantly racing to stay ahead by adopting the latest data tools and AI technologies. But with so many options and buzzwords, it’s easy to get lost in the excitement without knowing whether these tools truly serve your business. How can you ensure that your data stack is not only modern but sustainable and agile enough to adapt to changing needs? What does it take to build data products that deliver real value to your teams while driving innovation?
Adrian Estala is VP, Field Chief Data Officer and the host of Starburst TV. With a background in leading Digital and IT Portfolio Transformations, he understands the value of creating executive frameworks that focus on material business outcomes. Skilled with getting the most out of data-driven investments, Adrian is your trusted adviser to navigating complex data environments and integrating a Data Mesh strategy in your organization.
In the episode, Richie and Adrian explore the modern data stack, agility in data, collaboration between business and data teams, data products and differing ways of building them, data discovery and metadata, data quality, career skills for data practitioners and much more.
Links Mentioned in the Show:
StarburstConnect with AdrianCareer Track: Data Engineer in PythonRelated Episode: How this Accenture CDO is Navigating the AI RevolutionRewatch sessions from RADAR: AI EditionNew to DataCamp?
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AI is becoming a key tool in industries far beyond just tech. From automating tasks in the movie industry to revolutionizing drug development in life sciences, AI is transforming how we work. But with this growth comes important questions: How is AI really impacting jobs? Are we just increasing efficiency, or are we replacing human roles? And how can companies effectively store and leverage the vast amounts of data being generated every day to gain a competitive advantage?
Jamie Lerner is the President and CEO of Quantum, a company specializing in data storage, management, and protection. Since taking the helm in 2018, Lerner has steered Quantum towards innovative solutions for video and unstructured data. His leadership has been marked by strategic acquisitions and product launches that have significantly enhanced the company's market position. Before joining Quantum, Jamie worked at Cisco, Seagate, CITTIO, XUMA, and Platinum Technology. At Quantum, Lerner has been instrumental in shifting the company's focus towards data storage, management, and protection for video and unstructured data, driving innovation and strategic acquisitions to enhance its market position.
In the episode, Richie and jamie explore AI in subtitling, translation, and the movie industry at large, AI in sports, AI in business and scientific research, AI ethics, infrastructure and data management, video and image data in business, challenges of working with AI in video, excitement vs fear in AI and much more.
Links Mentioned in the Show:
QuantumConnect with JamieCareer Track: Data Engineer in PythonRelated Episode: Seeing the Data Layer Through Spatial Computing with Cathy Hackl and Irena CroninRewatch sessions from RADAR: AI EditionNew to DataCamp?
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The sheer number of tools and technologies that can infiltrate your work processes can be overwhelming. Choosing the right ones to invest in is critical, but how do you know where to start? What steps should you take to build a solid, scalable data infrastructure that can handle the growth of your business? And with AI becoming a central focus for many organizations, how can you ensure that your data strategy is aligned to support these initiatives? It’s no longer just about managing data; it’s about future-proofing your organization.
Taylor Brown is the COO and Co-Founder of Fivetran, the global leader in data movement. With a vision to simplify data connectivity and accessibility, Taylor has been instrumental in transforming the way organizations manage their data infrastructure. Fivetran has grown rapidly, becoming a trusted partner for thousands of companies worldwide. Taylor's expertise in technology and business strategy has positioned Fivetran at the forefront of the data integration industry, driving innovation and empowering businesses to harness the full potential of their data. Prior to Fivetran, Taylor honed his skills in various tech startups, bringing a wealth of experience and a passion for problem-solving to his entrepreneurial ventures.
In the episode, Richie and Taylor explore the biggest challenges in data engineering, how to find the right tools for your data stack, defining the modern data stack, federated data, data fabrics, data meshes, data strategy vs organizational structure, self-service data, data democratization, AI’s impact on data and much more.
Links Mentioned in the Show:
FivetranConnect with TaylorCareer Track: Data Engineer in PythonRelated Episode: Effective Data Engineering with Liya Aizenberg, Director of Data Engineering at AwayRewatch sessions from RADAR: AI EditionNew to DataCamp?
Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business
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Building and managing AI products comes with its own set of unique challenges. Especially when they are under intense scrutiny like mobile and home assistants have dealt with in recent years. From dealing with the unpredictable nature of machine learning models to ensuring that your product is both ethical and user-friendly, the path to success isn’t always clear. But how do you navigate these complexities and still deliver a product that meets business goals? What key steps can you take to align AI innovation with measurable outcomes and long-term success?
Marily Nika is one of the world's leading thinkers on product management for artificial intelligence. At Google, she manages the generative AI product features for Google Assistant. Marily also founded AI Product Academy, where she runs a BootCamp on AI product management, and she teaches the subject on Maven. Previously, Marily was an AI Product Lead in Meta's Reality Labs, and the AI Product Lead for Google Glass. She is also an Executive Fellow at Harvard Business School.
In the episode, Richie and Marily explore the unique challenges of AI product management, experimentation, ethical considerations in AI product management, collaboration, skills needed to succeed in AI product development, the career path to work in AI as a Product Manager, key metrics for AI products and much more.
Links Mentioned in the Show:
Komo AIConnect with MarilyMarily’s Course: AI Product Management Bootcamp with CertificationSkill Track: AI Business FundamentalsRelated Episode: Building Human-Centered AI Experiences with Haris Butt, Head of Product Design at ClickUpRewatch sessions from RADAR: AI EditionNew to DataCamp?
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Every organization today is exploring generative AI to drive value and push their business forward. But a common pitfall is that AI strategies often don’t align with business objectives, leading companies to chase flashy tools rather than focusing on what truly matters. How can you avoid these traps and ensure your AI efforts are not only innovative but also aligned with real business value?
Leon Gordon, is a leader in data analytics and AI. A current Microsoft Data Platform MVP based in the UK, founder of Onyx Data. During the last decade, he has helped organizations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence and big data. Leon is an Executive Contributor to Brainz Magazine, a Thought Leader in Data Science for the Global AI Hub, chair for the Microsoft Power BI – UK community group and the DataDNA data visualization community as well as an international speaker and advisor.
In the episode, Adel and Leon explore aligning AI with business strategy, building AI use-cases, enterprise AI-agents, AI and data governance, data-driven decision making, key skills for cross-functional teams, AI for automation and augmentation, privacy and AI and much more.
Links Mentioned in the Show:
Onyx DataConnect with LeonLeon’s Linkedin Course - How to Build and Execute a Successful Data StrategySkill Track: AI Business FundamentalsRelated Episode: Generative AI in the Enterprise with Steve Holden, Senior Vice President and Head of Single-Family Analytics at Fannie MaeRewatch sessions from RADAR: AI EditionNew to DataCamp?
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AI has rapidly emerged as an incredibly transformative technology, and nowhere has its impact been felt more unexpectedly than in the creative arts. Just a decade ago, few would have predicted that AI would evolve from automating routine tasks to generating paintings, music, and even poetry. Yet today, the role of AI in the arts has entered mainstream conversations, even contributing to the debates seen in last year’s Hollywood strikes.
Kent Kersey is a creative technologist who has served as a Product and Business leader in startups across B2B, B2C, and Enterprise SaaS. He is the founder and CEO of Invoke, an open-source Enterprise platform built to empower creatives to co-create with custom/fine-tuned AI products.
In the episode, Adel and Kent explore intellectual property and AI, the legal landscape surrounding AI models, open vs closed-source models, the future of creative teams and GenAI, innovations in GenAI, the role of artists in an AI-world, GenAI’s impact on the future of entertainment and much more.
Links Mentioned in the Show:
InvokeHow to Use Midjourney: A Comprehensive Guide to AI-Generated Artwork CreationCourse: Generative AI ConceptsRelated Episode: Seeing the Data Layer Through Spatial Computing with Cathy Hackl and Irena CroninRewatch sessions from RADAR: AI EditionNew to DataCamp?
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With AI tools constantly evolving, the potential for innovation seems limitless. But with great potential comes significant costs, and the question of efficiency and scalability becomes crucial. How can you ensure that your AI models are not only pushing boundaries but also delivering results in a cost-effective way? What strategies can help reduce the financial burden of training and deploying models, while still driving meaningful business outcomes?
Natalia Vassilieva is the VP & Field CTO of ML at Cerebras Systems. Natalia has a wealth of experience in research and development in natural language processing, computer vision, machine learning, and information retrieval. As Field CTO, she helps drive product adoption and customer engagement for Cerebras Systems' wafer-scale AI chips. Previously, Natalia was a Senior Research Manager at Hewlett Packard Labs, leading the Software and AI group. She also served as the head of HP Labs Russia leading research teams focused on developing algorithms and applications for text, image, and time-series analysis and modeling. Natalia has an academic background, having been a part-time Associate Professor at St. Petersburg State University and a lecturer at the Computer Science Center in St. Petersburg, Russia. She holds a PhD in Computer Science from St. Petersburg State University.
Andy Hock is the Senior VP, Product & Strategy at Cerebras Systems. Andy runs the product strategy and roadmap for Cerebras Systems, focusing on integrating AI research, hardware, and software to accelerate the development and deployment of AI models. He has 15 years of experience in product management, technical program management, and enterprise business development; over 20 years of experience in research, algorithm development, and data analysis for image processing; and 9 years of experience in applied machine learning and AI. Previously he was Product Management lead for Data and Analytics for Terra Bella at Google, where he led the development of machine learning-powered data products from satellite imagery. Earlier, he was Senior Director for Advanced Technology Programs at Skybox Imaging (which became Terra Bella following its acquisition by Google in 2014), and before that was a Senior Program Manager and Senior Scientist at Arete Associates. He has a Ph.D. in Geophysics and Space Physics from the University of California, Los Angeles.
In the episode, Richie, Natalia and Andy explore the dramatic recent progress in generative AI, cost and infrastructure challenges in AI, Cerebras’ custom AI chips and other hardware innovations, quantization in AI models, mixture of experts, RLHF, relevant AI use-cases, centralized vs decentralized AI compute, the future of AI and much more.
Links Mentioned in the Show:
CerebrasCerebras Launches the World’s Fastest AI InferenceConnect with Natalia and AndyCourse: Implementing AI Solutions in BusinessRewatch sessions from RADAR: AI EditionNew to DataCamp?
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In healthcare, data is becoming one of the most valuable tools for improving patient care and reducing costs. But with massive amounts of information and complex systems, how do organizations turn that data into actionable insights? How can AI and machine learning be used to create more transparency and help patients make better decisions? And more importantly, how can we ensure that these technologies make healthcare more efficient and affordable for everyone involved?
Travis Dalton is the President and CEO at Multiplan overseeing the execution of the company's mission and growth strategy. He has 20 years of leadership experience, with a focus on reducing the cost of healthcare, and enabling better outcomes for patients and healthcare providers. Previously, he was a General Manager and Executive VP at Oracle Health.
Jocelyn Jiang is the Vice President of Data & Decision Science at MultiPlan, a role she has held since 2023. In her position, she is responsible for leading the data and analytics initiatives that drive the company’s strategic growth and enhance its service offerings in the healthcare sector. Jocelyn brings extensive experience from her previous roles in healthcare and data science, including her time at EPIC Insurance Brokers & Consultants and Aon, where she worked in various capacities focusing on health and welfare consulting and actuarial analysis.
In the episode, Richie, Travis and Jocelyn explore the US healthcare system and the industry-specific challenges professionals face, the role of data in healthcare, ML and data science in healthcare, the future potential of healthcare tech, the global application of healthcare data solutions and much more.
Links Mentioned in the Show:
MultiplanPlanOptix: Providing Innovative Healthcare Price Transparency Using a Data Mining Service on Claims Data Can Reveal Significant OverpaymentsConnect with Travis and JocelynCourse: Intro to Data PrivacyRelated Episode: Data & AI for Improving Patient Outcomes with Terry Myerson, CEO at TruvetaRewatch sessions from RADAR: AI EditionNew to DataCamp?
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As AI becomes more accessible, a growing question is: should machine learning experts always be the ones training models, or is there a better way to leverage other subject matter experts in the business who know the use-case best? What if getting started building AI apps required no coding skills? As businesses look to implement AI at scale, what part can no-code AI apps play in getting projects off the ground, and how feasible are smaller, tailored solutions for department specific use-cases?
Birago Jones is the CEO at Pienso. Pienso is an AI platform that empowers subject matter experts in various enterprises, such as business analysts, to create and fine-tune AI models without coding skills. Prior to Pienso, Birago was a Venture Partner at Indicator Ventures and a Research Assistant at MIT Media Lab where he also founded the Media Lab Alumni Association.
Karthik Dinakar is a computer scientist specializing in machine learning, natural language processing, and human-computer interaction. He is the Chief Technology Officer and co-founder at Pienso. Prior to founding Pienso, Karthik held positions at Microsoft and Deutsche Bank. Karthik holds a doctoral degree from MIT in Machine Learning.
In the episode, Richie, Birago and Karthik explore why no-code AI apps are becoming more prominent, uses-cases of no-code AI apps, the steps involved in creating an LLM, the benefits of small tailored models, how no-code can impact workflows, cost in AI projects, AI interfaces and the rise of the chat interface, privacy and customization, excitement about the future of AI, and much more.
Links Mentioned in the Show:
PiensoGoogle Gemini for BusinessConnect with Birago and KarthikAndreesen Horowitz Report: Navigating the High Cost of AI ComputeCourse: Artificial Intelligence (AI) StrategyRelated Episode: Designing AI Applications with Robb Wilson, Co-Founder & CEO at Onereach.aiRewatch sessions from RADAR: AI EditionNew to DataCamp?
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We’ve all met someone with a limiting belief, someone who describes their relationship with data as: “I’m not a data person” or “I can’t tell a data story.” Oftentimes, this mindset starts in childhood. Data storytelling is an incredible vehicle to challenge and reshape these beliefs early on. Imagine if kids could develop the skills to ask the right questions, interpret data, and tell powerful stories with it from a young age. How can we introduce children to data storytelling in a fun and engaging way?
Cole Nussbaumer Knaflic has always had a penchant for turning data into pictures and into stories. She is CEO of Storytelling with Data, the author of the best-selling books, Storytelling with Data: a Data Visualization Guide for Business Professionals, Storytelling with Data: Let’s Practice!, and Storytelling with You: Plan, Create, and Deliver a Stellar Presentation. For more than a decade, Cole and her team have delivered interactive learning sessions sought after by data-minded individuals, companies, and philanthropic organizations all over the world. They also help people create graphs that make sense and weave them into compelling stories through the popular SWD community, blog, podcast, and videos.
In the episode, Adel and Cole explore Cole’s book Daphne Draws Data, challenging limiting beliefs that can develop during childhood, why early exposure to data literacy is important, engaging with children using data, adapting complex topics, data storytelling for adults, data visualization, building a data storytelling culture, the future of data storytelling in the age of AI, and much more.
Links Mentioned in the Show:
Cole’s Book: Daphne Draws DataStorytelling with DataConnect with ColeSkill Track: Data StorytellingRelated Episode: Navigating Parenthood with Data with Emily OsterRewatch sessions from RADAR: AI EditionNew to DataCamp?
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Lot’s of AI use-cases can start with big ideas and exciting possibilities, but turning those ideas into real results is where the challenge lies. How do you take a powerful model and make it work effectively in a specific business context? What steps are necessary to fine-tune and optimize your AI tools to deliver both performance and cost efficiency? And as AI continues to evolve, how do you stay ahead of the curve while ensuring that your solutions are scalable and sustainable?
Lin Qiao is the CEO and Co-Founder of Fireworks AI. She previously worked at Meta as a Senior Director of Engineering and as head of Meta's PyTorch, served as a Tech Lead at Linkedin, and worked as a Researcher and Software Engineer at IBM.
In the episode, Richie and Lin explore generative AI use cases, getting AI into products, foundational models, the effort required and benefits of fine-tuning models, trade-offs between models sizes, use cases for smaller models, cost-effective AI deployment, the infrastructure and team required for AI product development, metrics for AI success, open vs closed-source models, excitement for the future of AI development and much more.
Links Mentioned in the Show:
Fireworks.aiHugging Face - Preference Tuning LLMs with Direct Preference Optimization MethodsConnect with LinCourse - Artificial Intelligence (AI) StrategyRelated Episode: Creating Custom LLMs with Vincent Granville, Founder, CEO & Chief Al Scientist at GenAltechLab.comRewatch sessions from RADAR: AI EditionNew to DataCamp?
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The rapid rise of generative AI is changing how businesses operate, but with this change comes new challenges. How do you navigate the balance between innovation and risk, especially in a regulated industry? As organizations race to adopt AI, it’s crucial to ensure that these technologies are not only transformative but also responsible. What steps can you take to harness AI’s potential while maintaining control and transparency? And how can you build excitement and trust around AI within your organization, ensuring that everyone is ready to embrace this new era?
Steve Holden is the Senior Vice President and Head of Single-Family Analytics at Fannie Mae, leading a team of data science professionals, supporting loan underwriting, pricing and acquisition, securitization, loss mitigation, and loan liquidation for the company’s multi-trillion-dollar Single-Family mortgage portfolio. He is also responsible for all Generative AI initiatives across the enterprise. His team provides real-time analytic solutions that guide thousands of daily business decisions necessary to manage this extensive mortgage portfolio. The team comprises experts in econometric models, machine learning, data engineering, data visualization, software engineering, and analytic infrastructure design. Holden previously served as Vice President of Credit Portfolio Management Analytics at Fannie Mae. Before joining Fannie Mae in 1999, he held several analytic leadership roles and worked on economic issues at the Economic Strategy Institute and the U.S. Bureau of Labor Statistics.
In the episode Adel and Steve explore opportunities in generative AI, building a GenAI program, use-case prioritization, driving excitement and engagement for an AI-first culture, skills transformation, governance as a competitive advantage, challenges of scaling AI, future trends in AI, and much more.
Links Mentioned in the Show:
Fannie MaeSteve’s recent DataCamp Webinar: Bringing Generative AI to the EnterpriseVideo: Andrej Karpathy - [1hr Talk] Intro to Large Language ModelsSkill Track - AI Business FundamentalsRelated Episode: Generative AI at EY with John Thompson, Head of AI at EYRewatch sessions from RADAR: AI EditionJoin the DataFramed team!
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The pressure to innovate with AI is immense. There is seemingly a race against the clock for organizations to incorporate AI into their product offering, aside from continual digital transformation. As the speed of AI development accelerates, many organizations struggle to keep up, facing challenges from data readiness to changing traditional business processes. How can businesses ensure that their AI initiatives not only align with strategic goals but also foster real, tangible progress? What steps can leaders take to build AI fluency across their teams and turn potential into actionable outcomes?
Alison McCauley is a Best-Selling Author, Keynote Speaker, AI Strategist. She is Chief Advocacy Officer at Think with AI and Founder of Unblocked Future, a consultancy that leads the way in adopting emerging technologies, and has been collaborating with AI pioneers since 2010. With nearly 30 years of experience at the intersection of enterprise and disruptive innovation, Alison specializes in unlocking business value from cutting-edge technologies by focusing on the human aspects of change. She has been recognized as a Top Voice in AI, authored the book Unblocked, is a keynote speaker at global conferences, and her writings have appeared in Harvard Business Review, Forbes, and Venture Beat. Additionally, over 90,000 students have taken her LinkedIn course.
In the episode, Richie and Alison explore digital transformation and AI’s role in it, strategic alignment and shifting mindsets, AI fluency, challenges in data readiness, organizational resistance fuelled by fear, the role of management in AI transformation, practical steps to avoid AI risks, the long term impact of AI in the future and much more.
Links Mentioned in the Show:
Think with AIUnlocked FutureUnblocked: How Blockchains Will Change Your Business (and What to Do About It)Connect with AlisonCourse - Artificial Intelligence (AI) StrategyRelated Episode: How are Businesses Really Using AI? With Tathagat Varma, Global TechOps Leader at Walmart Global TechRewatch sessions from RADAR: AI EditionNew to DataCamp?
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