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 Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills witha...

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 Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

One of the prerequisites for being able to do great data analyses is that the data is well structured and clean and high quality. For individual projects, this is often annoying to get right. On a corporate level, it’s often a huge blocker to productivity. And then there’s healthcare data. When you consider all the healthcare records across the USA, or any other country for that matter, there are so many data formats created by so many different organizations, it’s frankly a horrendous mess. This is a big problem because there’s a treasure trove of data that researchers and analysts can’t make use of to answer questions about which medical interventions work or not. Bad data is holding back progress on improving everyone’s health. Terry Myerson is the CEO and Co-Founder of Truveta. Truveta enables scientifically rigorous research on more than 18% of the clinical care in the U.S. from a growing collective of more than 30 health systems. Previously, Terry enjoyed a 21-year career at Microsoft. As Executive Vice President, he led the development of Windows, Surface, Xbox, and the early days of Office 365, while serving on the Senior Leadership Team of the company. Prior to Microsoft, he co-founded Intersé, one of the earliest Internet companies, which Microsoft acquired in 1997.​ In the episode, Richie and Terry explore the current state of health records, challenges when working with health records, data challenges including privacy and accessibility, data silos and fragmentation, AI and NLP for fragmented data, regulatory grade AI, ongoing data integration efforts in healthcare, the future of healthcare and much more.  Links Mentioned in the Show: TruvetaConnect with TerryHIPAACourse - Introduction to Data PrivacyRelated Episode: Using AI to Improve Data Quality in HealthcareRewatch sessions from RADAR: AI 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

Guardrails are not something we actively use in our day-to-day lives, they’re in place to keep us safe when we lack the control needed to keep us on course, and for that, they are essential. Navigating the complexities of decision-making in AI and data can be challenging, especially on a global scale when many are searching for any sort of competitive advantage. Every choice you make can have significant impacts, and having the right frameworks, ethics and guardrails in place are crucial. But how do you create systems that guide decisions without stifling creativity or flexibility? What practices can you employ to ensure your team consistently make better choices and flourish in the age of AI? Viktor Mayer-Schönberger is a distinguished Professor of Internet Governance and Regulation at the Oxford Internet Institute, University of Oxford. With a career spanning over decades, his research focuses on the role of information in a networked economy. He previously served on the faculty of Harvard’s Kennedy School of Government for ten years and has authored several influential books, including the award-winning “Delete: The Virtue of Forgetting in the Digital Age” and the international bestseller “Big Data.” Viktor founded Ikarus Software in 1986, where he developed Virus Utilities, Austria’s best-selling software product. He has been recognized as a Top-5 Software Entrepreneur in Austria and has served as a personal adviser to the Austrian Finance Minister on innovation policy. His work has garnered global attention, featuring in major outlets like the New York Times, BBC, and The Economist. Viktor is also a frequent public speaker and an advisor to governments, corporations, and NGOs on issues related to the information economy. In the episode, Richie and Viktor explore the definition of guardrails, characteristics of good guardrails, guardrails in business contexts, life-or-death decision-making, principles of effective guardrails, decision-making and cognitive bias, uncertainty in decision-making, designing guardrails, AI and the implementation of guardrails, and much more. Links Mentioned in the Show: Guardrails: Guiding Human Decisions in the Age of AI by Urs Gasser and Viktor Mayer-SchönbergerBook - The Checklist Manifesto by Atul GawandeConnect with ViktorCourse - AI EthicsRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistRewatch sessions from RADAR: AI 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

Doing sales better is perhaps the most direct route to making more revenue, so it should be a priority for every business. B2B sales is often very complex, with a mix of emails and video calls and prospects interacting with your website and social content. And you often have multiple people making decisions about a purchase. All this generates a massive data—or, more accurately, a mess of data—which very few sales teams manage to harness effectively. How can sales teams can make use of data, software, and AI to clean up this mess, work more effectively, and most of all, crush those quarterly targets?  Ellie Fields is the Chief Product and Engineering Officer at Salesloft leading Product Management, Engineering, and Design. Ellie previously led development teams at Tableau responsible for product strategy and engineering for collaboration and mobile portfolio. Ellie also launched and led Tableau Public. In the episode Richie and Ellie explore the digital transformation of sales, how sales technology helps buyers and sellers, metrics for sales success, activity vs outcome metrics, predictive forecasting, AI, customizing sales processes, revenue orchestration, how data impacts sales and management, future trends in sales, and much more.  Links Mentioned in the Show: SalesloftConnect with EllieForrester ResearchCourse - Understanding the EU AI ActRelated Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at TescoRewatch sessions from RADAR: AI 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

One of the big use cases of generative AI is having small applications to solve specific tasks. These are known as AI agents or AI assistants. Since they’re small and narrow in scope, you probably want to create and use lots of them, which means you need to be able to create them cheaply and easily. I’m curious as to how you go about doing this from an organizational point of view. Who needs to be involved? What’s the workflow and what technology do you need? Dmitry Shapiro is the CEO of MindStudio. He was previously the CTO at MySpace and a product manager at Google. Dmitry is also a serial entrepreneur, having founded the web-app development platform Koji, acquired by Linktree, and Veoh Networks, an early YouTube competitor. He has extensive experience in building and managing engineering, product, and AI teams. In the episode, Richie and Dmitry explore generative AI applications, AI in SaaS, approaches to AI implementation, selecting processes for automation, changes in sales and marketing roles, MindStudio, AI governance and privacy concerns, cost management, the limitations and future of AI assistants, and much more. Links Mentioned in the Show: MindStudioConnect with Dmitry[Webinar] Dmitry at RADAR: From Learning to Earning: Navigating the AI Job LandscapeRelated Episode: Designing AI Applications with Robb Wilson, Co-Founder & CEO at Onereach.aiRewatch sessions from RADAR: AI 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

Perhaps the biggest complaint about generative AI is hallucination. If the text you want to generate involves facts, for example, a chatbot that answers questions, then hallucination is a problem. The solution to this is to make use of a technique called retrieval augmented generation, where you store facts in a vector database and retrieve the most appropriate ones to send to the large language model to help it give accurate responses. So, what goes into building vector databases and how do they improve LLM performance so much? Ram Sriharsha is currently the CTO at Pinecone. Before this role, he was the Director of Engineering at Pinecone and previously served as Vice President of Engineering at Splunk. He also worked as a Product Manager at Databricks. With a long history in the software development industry, Ram has held positions as an architect, lead product developer, and senior software engineer at various companies. Ram is also a long time contributor to Apache Spark.  In the episode, Richie and Ram explore common use-cases for vector databases, RAG in chatbots, steps to create a chatbot, static vs dynamic data, testing chatbot success, handling dynamic data, choosing language models, knowledge graphs, implementing vector databases, innovations in vector data bases, the future of LLMs and much more.  Links Mentioned in the Show: PineconeWebinar - Charting the Path: What the Future Holds for Generative AICourse - Vector Databases for Embeddings with PineconeRelated Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at PineconeRewatch sessions from RADAR: AI 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

By now, many of us are convinced that generative AI chatbots like ChatGPT are useful at work. However, many executives are rightfully worried about the risks from having business and customer conversations recorded by AI chatbot platforms. Some privacy and security-conscious organizations are going so far as to block these AI platforms completely. For organizations such as EY, a company that derives value from its intellectual property, leaders need to strike a balance between privacy and productivity.  John Thompson runs the department for the ideation, design, development, implementation, & use of innovative Generative AI, Traditional AI, & Causal AI solutions, across all of EY's service lines, operating functions, geographies, & for EY's clients. His team has built the world's largest, secure, private LLM-based chat environment. John also runs the Marketing Sciences consultancy, advising clients on monetization strategies for data. He is the author of four books on data, including "Data for All' and "Causal Artificial Intelligence". Previously, he was the Global Head of AI at CSL Behring, an Adjunct Professor at Lake Forest Graduate School of Management, and an Executive Partner at Gartner. In the episode, Richie and John explore the adoption of GenAI at EY, data privacy and security, GenAI use cases and productivity improvements, GenAI for decision making, causal AI and synthetic data, industry trends and predictions and much more.  Links Mentioned in the Show: Azure OpenAICausality by Judea Pearl[Course] AI EthicsRelated Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at TescoCatch John talking about AI Maturity this SeptemberRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

There’s been a lot of pressure to add AI to almost every digital tool and service recently, and two years into the AI hype cycle, we’re seeing two types of problems. The first is organizations that haven’t done much yet with AI because they don’t know where to start. The second is organizations that rushed into AI and failed because they didn’t know what they were doing. Both are symptoms of the same problem: not having an AI strategy and not understanding how to tactically implement AI. There’s a lot to consider around choosing the right project and putting processes and skilled talent in place, not to mention worrying about costs and return on investment. Tathagat Varma is the Global TechOps Leader at Walmart Global Tech. Tathagat is responsible for leading strategic business initiatives, enterprise agile transformation, technical learning and enablement, strategic technical initiatives, startup ecosystem engagement, and internal events across Walmart Global Tech. He also provides support to horizontal technical and internal innovation programs in the company. Starting as a Computer Scientist with DRDO, and with an overall experience of 27 years, Tathagat has played significant technical and leadership roles in establishing and growing organizations like NerdWallet, ChinaSoft International, McAfee, Huawei, Network General, NetScout System, [24]7 Innovations Labs and Yahoo!, and played key engineering roles at Siemens and Philips. In the episode, Richie and Tathagat explore failures in AI adoption, the role of leadership in AI adoption, AI strategy and business objective alignment, investment and timeline for AI projects, identifying starter AI projects, skills for AI success, building a culture of AI adoption, the potential of AI and much more.  Links Mentioned in the Show: Walmart Global TechConnect with Tathagat[Course] Data Governance ConceptsRelated Episode: How Walmart Leverages Data & AI with Swati Kirti, Sr Director of Data Science at WalmartRewatch sessions from RADAR: AI 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

One of the most annoying conversations about data that happens far too often is: “Can you do an analysis and answer this business problem for me?” “Sure, where’s the data?” “I don’t know. Probably in one of our databases.” At this point more time is spent hunting for data than actually analyzing it. Rather than grumbling about it, it would obviously be more productive to learn how to solve data discoverability issues. What’s the best way to properly document data sets? How can you avoid spending all your time maintaining dashboards that no one actually uses?  Shinji Kim is the Founder & CEO of Select Star, an automated data discovery platform that helps you understand your data. Previously, she was the CEO of Concord Systems (concord.io), a NYC-based data infrastructure startup acquired by Akamai Technologies in 2016. She led building Akamai’s new IoT data platform for real-time messaging, log processing, and edge computing. Prior to Concord, Shinji was the first Product Manager hired at Yieldmo, where she led the Ad Format Lab, A/B testing, and yield optimization. Before Yieldmo, she was analyzing data and building enterprise applications at Deloitte Consulting, Facebook, Sun Microsystems, and Barclays Capital. Shinji studied Software Engineering at University of Waterloo and General Management at Stanford GSB. She advises early stage startups on product strategy, customer development, and company building. In the episode, Richie and Shinji explore the importance of data governance, the utilization of data, data quality, challenges in data usage, why documentation matters, metadata and data lineage, improving collaboration between data and business teams, data governance trends to look forward to, and much more.  Links Mentioned in the Show: Select StarConnect with Shinji[Course] Data Governance ConceptsRelated Episode: Making Data Governance Fun with Tiankai Feng, Data Strategy & Data Governance Lead at ThoughtWorksRewatch sessions from RADAR: AI 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

This special episode of DataFramed was made in collaboration with Analytics on Fire! Nowadays, the hype around generative AI is only the tip of the iceberg. There are so many ideas being touted as the next big thing that it’s difficult to keep up. More importantly, it’s challenging to discern which ideas will become the next ChatGPT and which will end up like the next NFT. How do we cut through the noise? Mico Yuk is the Community Manager at Acryl Data and Co-Founder at Data Storytelling Academy. Mico is also an SAP Mentor Alumni, and the Founder of the popular weblog, Everything Xcelsius and the 'Xcelsius Gurus’ Network. She was named one of the Top 50 Analytics Bloggers to follow, as-well-as a high-regarded BI influencer and sought after global keynote speaker in the Analytics ecosystem.  In the episode, Richie and Mico explore AI and productivity at work, the future of work and AI, GenAI and data roles, AI for training and learning, training at scale, decision intelligence, soft skills for data professionals, genAI hype and much more.  Links Mentioned in the Show: Analytics on Fire PodcastData Visualization for Dummies by Mico Yuk and Stephanie DiamondConnect with Miko[Skill Track] AI FundamentalsRelated Episode: What to Expect from AI in 2024 with Craig S. Smith, Host of the Eye on A.I PodcastRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Despite GPT, Claude, Gemini, LLama and the other host of LLMs that we have access to, a variety of organizations are still exploring their options when it comes to custom LLMs. Logging in to ChatGPT is easy enough, and so is creating a 'custom' openAI GPT, but what does it take to create a truly custom LLM? When and why might this be useful, and will it be worth the effort? Vincent Granville is a pioneer in the AI and machine learning space, he is Co-Founder of Data Science Central, Founder of MLTechniques.com, former VC-funded executive, author, and patent owner. Vincent’s corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. He is also a former post-doc at Cambridge University and the National Institute of Statistical Sciences. Vincent has published in the Journal of Number Theory, Journal of the Royal Statistical Society, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is the author of multiple books, including “Synthetic Data and Generative AI”. In the episode, Richie and Vincent explore why you might want to create a custom LLM including issues with standard LLMs and benefits of custom LLMs, the development and features of custom LLMs, architecture and technical details, corporate use cases, technical innovations, ethics and legal considerations, and much more.  Links Mentioned in the Show: Read Articles by VincentSynthetic Data and Generative AI by Vincent GranvilleConnect with Vincent on Linkedin[Course] Developing LLM Applications with LangChainRelated Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at PineconeRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with this role often including software engineering tasks. In particular, one of the key functions of a full stack data scientist is to take machine learning models and get them into production inside software. So, what separates projects from production? Savin Goyal is the Co-Founder & CTO at Outerbounds. In addition to his work at Outerbounds, Savin is the creator of the open source machine learning management platform Metaflow. Previously Savin has worked as a Software Engineer at Netflix and LinkedIn. In the episode, Richie and Savin explore the definition of production in data science, steps to move from internal projects to production, the lifecycle of a machine learning project, success stories in data science, challenges in quality control, Metaflow, scalability and robustness in production, AI and MLOps, advice for organizations and much more.  Links Mentioned in the Show: OuterboundsMetaflowConnect with Savin on Linkedin[Course] Developing Machine Learning Models for ProductionRelated Episode: Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling AuthorRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Conversations about the future of AI tend to be rather divisive, with opinions ranging from artificial superintelligence arriving to save the world, or to eradicate humanity. There's a sense that the latter is undesirable and that something ought to be done to prevent it. In order to get from that vague feeling to having steps that are practical in order to shape the future of AI, we can draw lessons from history. Looking back, to look ahead.  Verity Harding is a globally recognised leader at the intersection of technology, politics and public policy. She is Founder of Formation Advisory Ltd, a bespoke technology consultancy firm, and Director of the AI & Geopolitics Project at Cambridge University's Bennett Institute for Public Policy. Her debut book ‘AI Needs You’ was published by Princeton University Press in March 2024. In the episode, Richie and Verity explore why history is important for the future of AI, the space race, the role of AI in society, historical analogies including comparisons of AI to the cold war, the evolution of the internet, IVF, the role of government and regulation, multi-stakeholder models and much more.  Links Mentioned in the Show: Verity’s Book: AI Needs YouConnect with Verity on LinkedinThe Warnock Committee Outer Space Treaty[Skill Track] Developing AI ApplicationsRelated Episode: The History of Data and AI, and Where It's Headed with Cristina Alaimo, Assistant Professor at Luiss Guido Carli UniversityRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

All the hype around generative AI means that every software maker seems to be stuffing chat interfaces into their products whenever they can. For the most part, the jury is still out on whether this is a good idea or not. However, design goes deeper than just the user interface, so it’s also useful to know about how the designs interact with the rest of the software. Once you move beyond chatbots into things like agents, there are also thorny questions around which bits of your workflow should still be done by a human, and which bits can be completely automated. True insight in this context lies in a gray area, across software, UX and AI. Robb is an AI researcher, technologist, designer, innovator, serial entrepreneur, and author. He is a contributor to Harvard Business Review and the visionary behind, OneReach.ai, the award winning conversational artificial intelligence platform that ranked highest in Gartner's Critical Capabilities Report for Enterprise Conversational AI Platforms. He earned an Academy Award nomination for technical achievement as well as over 130 innovation, design, technology, and artificial intelligence awards, with five in 2019 including AI Company of the Year and Hot AI Technology of the Year. Robb is a pioneer in the user research and technology spaces. He founded EffectiveUI, a user experience and technology research consultancy for the Fortune 500, which was acquired by WPP and integrated into the core of Ogilvy’s digital experience practice. He also created UX Magazine, one of the first and largest XD (experience design) thought leadership communities.  In the episode, Richie and Robb explore chat interfaces in software, the advantages of chat interfaces over other methods of interaction with data & AI products, geospatial vs language memory, good vs bad chat interfaces, the importance of a human in the loop, personality in chatbots, handling hallucinations and bad responses, scaling chatbots, agents vs chatbots, ethical considerations for AI and chatbots and much more.  Links Mentioned in the Show: Onereach.aiInvisible Machines PodcastGartner: The Executive Guide to Hyperautomation[Skill Track] Developing AI ApplicationsRelated Episode: Building Human-Centered AI Experiences with Haris Butt, Head of Product Design at ClickUpSign up to RADAR: AI 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

Loyalty schemes are a hallmark of established retailers—not only do they build consumer trust, they are intelligent and constantly evolving, and Tesco’s Clubcard is the UK’s favorite retail loyalty program. The effects of these discounts are far-reaching, especially for families who rely on getting the best deals to make the most of their money. As Tesco’s tagline goes, every little helps. In turn, the identification and specific details of discounted products can have a profound impact on how consumers view the largest supermarket retailer in the United Kingdom, as well as the operational costs and profits that shareholders are concerned with. How do data and AI inform these offers, what goes into the enterprise-scale analytics that keeps Tesco’s Clubcard the UK’s favorite? Venkat Raghavan is Director of Analytics and Science at Tesco. Venkat’s area of expertise is customer analytics, having been very heavily involved with the Tesco Clubcard loyalty program. Venkat also set up an analytics center of excellence to help break down data silos between teams. Previously, he was a Director of Analytics at Boston Consulting Group and Senior Director for Advanced Analytics & AI for Manthan and a Cross Industry Delivery Leader at Mu Sigma. In the episode, Richie and Venkat explore Tesco’s use of data, the introduction of the clubcard scheme, Tesco’s data-driven innovations in online food retail, understanding customer behavior through loyalty programs and in-app interactions, improving customer experience at Tesco, operating a cohesive data intelligence platform that leverages multiple data sources, communication between data and business teams, pricing and cost management, the challenges of data science at scale, the future of data and much more.  Links Mentioned in the Show: Tesco ClubcardMcKinsey: State of Grocery Europe 2024[Course] Data Science for BusinessRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of AlteryxSign up to RADAR: AI 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

Spatial computing is revolutionizing the way we interact with digital and physical worlds, but its adoption comes with questions about practicality and return on investment. As businesses explore this cutting-edge technology, they must consider how it can enhance productivity and streamline operations. What are the best strategies to integrate spatial computing into your current systems? How can you ensure that it not only boosts efficiency but also delivers measurable benefits to your bottom line?  Cathy Hackl is a web3 and metaverse strategist, tech futurist, speaker and author. She's worked with metaverse-related companies such as HTC VIVE, Magic Leap, and AWS, and currently consults with some of the world's leading brands, including P&G, Clinique, Ralph Lauren, Orlando Economic Partnership and more. Hackl is one of the world's first Chief Metaverse Officers and the co-founder of Journey, where she works with luxury, fashion, and beauty brands to create successful metaverse and web3 strategies and helps them build worlds in platforms like Roblox, Fortnite, Decentraland, The Sandbox, and beyond. She is widely regarded as one of the leading thinkers on the Metaverse. Irena Cronin is SVP of Product for DADOS Technology, which is making an Apple Vision Pro data analytics and visualization app. She is also the CEO of Infinite Retina, which helps companies develop and implement AI, AR, and other new technologies for their businesses. Before this, she worked as an equity research analyst and gained extensive experience in evaluating both public and private companies. In the episode, Richie, Cathy and Irina explore spatial computing, the current viability of spacial computing and it's prominence alongside the release of Apple's Vision Pro, expected effects of spatial computing on gaming and entertainment, industrial applications as well as data visualization and AI integration opportunities of spatial computing, how businesses can leverage spatial computing, future developments in the space and much more.  Links Mentioned in the Show: Cathy’s BookIrena’s BooksApple Vision ProMarvel Studios and ILM Immersive Announce 'What If...? - An Immersive Story'[Course] Artificial Intelligence (AI) StrategyRelated Episode: Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpotSign up to RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Memory, the foundation of human intelligence, is still one of the most complex and mysterious aspects of the brain. Despite decades of research, we've only scratched the surface of understanding how our memories are formed, stored, and retrieved. But what if AI could help us crack the code on memory? How might AI be the key to unlocking problems that have evaded human cognition for so long? Kim Stachenfeld is a Senior Research Scientist at Google DeepMind in NYC and Affiliate Faculty at the Center for Theoretical Neuroscience at Columbia University.  Her research covers topics in Neuroscience and AI. On the Neuroscience side, she study how animals build and use models of their world that support memory and prediction. On the Machine Learning side, she works on implementing these cognitive functions in deep learning models. Kim’s work has been featured in The Atlantic, Quanta Magazine, Nautilus, and MIT Technology Review. In 2019, she was named one of MIT Tech Review’s Innovators under 35 for her work on predictive representations in hippocampus.  In the episode, Richie and Kim explore her work on Google Gemini, the importance of customizability in AI models, the need for flexibility and adaptability in AI models, retrieval databases and how they improve AI response accuracy, AI-driven science, the importance of augmenting human capabilities with AI and the challenges associated with this goal, the intersection of AI, neuroscience and memory and much more.  Links Mentioned in the Show: DeepMindAlphaFoldDr James Whittington - A unifying framework for frontal and temporal representation of memoryPaper - Language models show human-like content effects onreasoning tasksKim’s Website[Course] Artificial Intelligence (AI) StrategyRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

In the fast-paced work environments we are used to, the ability to quickly find and understand data is essential. Data professionals can often spend more time searching for data than analyzing it, which can hinder business progress. Innovations like data catalogs and automated lineage systems are transforming data management, making it easier to ensure data quality, trust, and compliance. By creating a strong metadata foundation and integrating these tools into existing workflows, organizations can enhance decision-making and operational efficiency. But how did this all come to be, who is driving better access and collaboration through data? Prukalpa Sankar is the Co-founder of Atlan. Atlan is a modern data collaboration workspace (like GitHub for engineering or Figma for design). By acting as a virtual hub for data assets ranging from tables and dashboards to models & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Slack, BI tools, data science tools and more. A pioneer in the space, Atlan was recognized by Gartner as a Cool Vendor in DataOps, as one of the top 3 companies globally. Prukalpa previously co-founded SocialCops, world leading data for good company (New York Times Global Visionary, World Economic Forum Tech Pioneer). SocialCops is behind landmark data projects including India’s National Data Platform and SDGs global monitoring in collaboration with the United Nations. She was awarded Economic Times Emerging Entrepreneur for the Year, Forbes 30u30, Fortune 40u40, Top 10 CNBC Young Business Women 2016, and a TED Speaker. In the episode, Richie and Prukalpa explore challenges within data discoverability, the inception of Atlan, the importance of a data catalog, personalization in data catalogs, data lineage, building data lineage, implementing data governance, human collaboration in data governance, skills for effective data governance, product design for diverse audiences, regulatory compliance, the future of data management and much more.  Links Mentioned in the Show: AtlanConnect with Prukalpa[Course] Artificial Intelligence (AI) StrategyRelated Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at SnowflakeSign up to RADAR: AI 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

In today's fast-paced digital world, managing IT operations is more complex than ever. With the rise of cloud services, microservices, and constant software deployments, the pressure on IT teams to keep everything running smoothly is immense. But how do you keep up with the ever-growing flood of data and ensure your systems are always available? AIOps is the use of artificial intelligence to automate and scale IT operations. But what exactly is AIOps, and how can it transform your IT operations? Assaf Resnick is the CEO and Co-Founder of BigPanda. Before founding BigPanda, Assaf was an investor at Sequoia Capital, where he focused on early and growth-stage investing in software, internet, and mobile sectors. Assaf’s time at Sequoia gave him a front-row seat to the challenges of IT scale, complexity, and velocity faced by Operations teams in rapidly scaling and accelerating organizations. This is the problem that Assaf founded BigPanda to solve. In the episode, Richie and Assaf explore AIOps, how AIOps helps manage increasingly complex IT operations, how AIOps differs from DevOps and MLOps, examples of AIOps projects, a real world application of AIOps, the key benefits of AIOps, how to implement AIOps, excitement in the space, how GenAI is improving AIOps and much more.  Links Mentioned in the Show: BigPandaGartner: Market Guide for AIOps Platforms[Course] Implementing AI Solutions in BusinessRelated Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at SnowflakeSign up to RADAR: AI 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