Speedily adopting new technologies can give your business a competitive advantage, but with so much happening in the world of generative AI, it's difficult to know what to adopt. In this episode, Richie chats to two venture capitalists to get their view on the global AI landscape, where we are in the AI hype cycle, and how to adopt AI tech. Beyond this, we explore Rocketship.vc's use of data and algorithms to make investment decisions in early-stage startups. If our previous episode’s deep dive into 2024’s data & AI trends with VC Tom Tunguz got you excited about how investors are looking at the market at the moment, then this episode is sure to do the same. This time, we have twice the insight, thanks to our two guests. Madhu Shalini Iyer is a Managing Partner at Rocketship.vc, a Silicon Valley based fund investing globally. She was the Chief Data Officer of Gojek and helped grow the business into a $10 billion unicorn. In addition to being a board member, she started the Singapore office and played an active role in the strategy, new business development, and ‘data as a competitive advantage’. Prior to Gojek, Madhu was part of the founding team of Intuit’s Quickbooks Lending Platform. As the data science leader at Intuit, Madhu helped grow the platform to $300 million and holds 2 patents in the areas of user data augmented algorithms for financial inclusion. Madhu was also the Chief Data Officer for Ethoslending. There she built the underwriting platform and was responsible for all b2c revenue, resulting in $65 million gross market value per month. Madhu was further responsible for building and running the marketing team. Prior, Madhu was a partner at a $150m private equity fund, Stem Financial, in Hong Kong. She started her career as a senior data scientist with a leading think tank in Menlo Park, CA. Sailesh Ramakrishnan is also a Managing Partner at Rocketship.vc. Prior to Rocketship.vc, Sailesh was CTO and co-founder of LocBox (acquired by Square), a startup focussed on marketing for local businesses. Sailesh worked with Anand and Venky at their previous startup Kosmix, and continued on to Walmart as a Director of Engineering at @WalmartLabs. Before jumping into the startup world, Sailesh worked as a Computer Scientist at NASA Ames Research Center. Sailesh earned his Bachelors degree in Civil Engineering from IIT Madras, his Masters degree in Construction Management from Virginia Tech and another Master degree in Intelligent Systems from University of Pittsburgh. He was a Ph.D. candidate in Artificial Intelligence at the University of Michigan. In the episode, Richie, Madhu and Sailesh explore the generative AI revolution, categorizing generative AI tools, the impact of genAI across industries, investment philosophy and data-driven decision-making, the challenges and opportunities when investing in AI, future trends and predictions, regulatory and ethical considerations of AI, and much more. Links Mentioned in the Show: Rocketship.vc[Course] Implementing AI Solutions in BusinessRelated Episode: Inside Algorithmic Trading with Anthony Markham, Vice President, Quantitative Developer at Deutsche BankSign up to RADAR: AI Edition New to DataCamp? Learn on the go using thea href="https://www.datacamp.com/mobile" rel="noopener noreferrer"...
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Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !!
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Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal:
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Novo golpe mira profissionais de tecnologia com vagas de emprego falsas;
ChatGPT deve ganhar motor de busca para competir com Google:
Padre com inteligência artificial foi 'excomungado' após recomendar batismo em Gatorade.
Webinar Data Hackers + Thoughtworks (08.05 I 19h): GenAI para impulsionar resultados no Mercado Financeiro
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Rapid change seems to be the new norm within the data and AI space, and due to the ecosystem constantly changing, it can be tricky to keep up. Fortunately, any self-respecting venture capitalist looking into data and AI will stay on top of what’s changing and where the next big breakthroughs are likely to come from. We all want to know which important trends are emerging and how we can take advantage of them, so why not learn from a leading VC. Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs sat tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others. He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation. In the episode, Richie and Tom explore trends in generative AI, the impact of AI on professional fields, cloud+local hybrid workflows, data security, and changes in data warehousing through the use of integrated AI tools, the future of business intelligence and data analytics, the challenges and opportunities surrounding AI in the corporate sector. You'll also get to discover Tom's picks for the hottest new data startups. Links Mentioned in the Show: Tom’s BlogTheory VenturesArticle: What Air Canada Lost In ‘Remarkable’ Lying AI Chatbot Case[Course] Implementing AI Solutions in BusinessRelated 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
Data engineers and data scientists must manage pipelines for unstructured data to ensure healthy inputs for language models. Published at: https://www.eckerson.com/articles/why-and-how-data-engineers-will-enable-the-next-phase-of-generative-ai
Companies that adopt DataOps increase the odds of success by making GenAI data pipelines what they should be: modular, scalable, robust, flexible, and governed. Published: https://www.eckerson.com/articles/dataops-for-generative-ai-data-pipelines-part-ii-must-have-characteristics
Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !!
Aperte o play e ouça agora, o Data Hackers News dessa semana !
Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal:
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Webinar Data Hackers + Thoughtworks (08.05 I 19h): GenAI para impulsionar resultados no Mercado Financeiro Óculos da Ray-Ban e Meta agora têm IA multimodal
Google demite seu time de Python inteiro
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Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style!
In this episode, we're thrilled to have special guest Mehdi Ouazza diving into a plethora of hot tech topics: Mehdi Ouazza's Insights into his career, online community and working with DuckDB and MotherDuck.Demystifying DevRel: Definitions and distinctions in the realm of tech influence (dive deeper here).Terraform's Licensing Shift: Reactions to HashiCorp's recent changes and its new IBM collaboration, more details here.Github Copilot Workspace: Exploring the latest in AI-powered coding assistance, comparing with devin.ai and CodySnowflake's Arctic LLM: Discussing the latest enterprise AI capabilities and their real-world applications. Read more about Arctic - what it excels at, and how its performance was measuredMore legal kerfuffle in the GenAI realm: The ongoing legal debates around AI's use in creative industries, highlighted by a dispute over Drake’s use of late rapper Tupac’s AI-generated voice in diss track & the licensing deal between Financial Times and OpenAIFuture of Data Engineering: Examining the integration of LLMs into data engineering tools. Insights on prompt-based feature engineering and Databricks' English SDKAI in Music Creation: A little bonus with an AI generated song about Murilo, created with Suno
Generative AI is evolving quickly and presenting a breadth of options available for the media industry to enable efficiencies and service improvements.
Countless companies invest in their data quality, but often, the effort from their investment is not fully realized in the output. It seems like, despite the critical importance of data quality, data governance might be suffering from a branding issue. Data governance is sometimes looked at as the data police, but this is far from the truth. So, how can we change perspectives and introduce fun into data governance? Tiankai Feng is a Principal Data Consultant and Data Strategy & Data Governance Lead at Thoughtworks, He also works part-time as the Head of Marketing at DAMA Germany. Tiankai has had many data hats in his career—marketing data analyst, data product owner, analytics capability lead, and data governance leader for the last few years. He has found a passion for the human side of data—how to collaborate, coordinate, and communicate around data. TIankai often uses his music and humor to make data more approachable and fun. In the episode, Adel and Tiankai explore the importance of data governance in data-driven organizations, the challenges of data governance, how to define success criteria and measure the ROI of governance initiatives, non-invasive and creative approaches to data governance, the implications of generative AI on data governance, regulatory considerations, organizational culture and much more. Links Mentioned in the Show: Tiankai’s YouTube ChannelData Governance Fundamentals Cheat Sheet[Webinar] Unpacking the Fun in Data Governance: The Key to Scaling Data Quality[Course] Data Governance ConceptsRewatch sessions from RADAR: The Analytics 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
Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable.
Summary Generative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use. In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementDagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.Your host is Tobias Macey and today I'm interviewing Tsavo Knott about Pieces, a personal AI toolkit to improve the efficiency of developersInterview IntroductionHow did you get involved in machine learning?Can you describe what Pieces is and the story behind it?The past few months have seen an endless series of personalized AI tools launched. What are the features and focus of Pieces that might encourage someone to use it over the alternatives?model selectionsarchitecture of Pieces applicationlocal vs. hybrid vs. online modelsmodel update/delivery processdata preparation/serving for models in context of Pieces appapplication of AI to developer workflowstypes of workflows that people are building with piecesWhat are the most interesting, innovative, or unexpected ways that you have seen Pieces used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pieces?When is Pieces the wrong choice?What do you have planned for the future of Pieces?Contact Info LinkedInParting Question From your perspective, what is the biggest barrier to adoption of machine learning today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.Links PiecesNPU == Neural Processing UnitTensor ChipLoRA == Low Rank AdaptationGenerative Adversarial NetworksMistralEmacsVimNeoVimDartFlutte
Presentation on the opportunities and challenges of applying generative AI in finance.
Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society.
Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style!
In this episode, we're joined by special guest Vitale Sparacello, an MLflow Ambassador, to delve into a myriad of topics shaping the current and future landscape of AI and software development: MLflow Deep Dive: Exploring the MLflow Ambassador program, MLflow's role in promoting MLOps practices, and its expansion to support generative AI technologies.Introducing Llama 3: Discussing Meta's newest language model, Llama 3, its capabilities, and the nuanced policy of its distribution, featured on platforms like Groq. Read more here.Emerging AI Tools: Evaluating Open-Parse for advanced text parsing and debating the longevity of PDF documents in the age of advanced AI technologies.OpenTofu vs. Terraform Drama: Analyzing the ongoing legal dispute between Terraform and OpenTofu, with discussions around code ethics and links to OpenTofu's LinkedIn, and their official blog response.The Future of AI Devices: Are smartphones the endgame, or is there a future for specialized AI wearables? Speculating on the evolution of AI devices, focusing on the Human AI Pin review, Rabbit R1 and Dot Computer Go check out the Youtube video after so you don't miss out Murilo in that suit he promised (with a duck tie of course).
Summary
Generative AI has rapidly transformed everything in the technology sector. When Andrew Lee started work on Shortwave he was focused on making email more productive. When AI started gaining adoption he realized that he had even more potential for a transformative experience. In this episode he shares the technical challenges that he and his team have overcome in integrating AI into their product, as well as the benefits and features that it provides to their customers.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Andrew Lee about his work on Shortwave, an AI powered email client
Interview
Introduction How did you get involved in the area of data management? Can you describe what Shortwave is and the story behind it?
What is the core problem that you are addressing with Shortwave?
Email has been a central part of communication and business productivity for decades now. What are the overall themes that continue to be problematic? What are the strengths that email maintains as a protocol and ecosystem? From a product perspective, what are the data challenges that are posed by email? Can you describe how you have architected the Shortwave platform?
How have the design and goals of the product changed since you started it? What are the ways that the advent and evolution of language models have influenced your product roadmap?
How do you manage the personalization of the AI functionality in your system for each user/team? For users and teams who are using Shortwave, how does it change their workflow and communication patterns? Can you describe how I would use Shortwave for managing the workflow of evaluating, planning, and promoting my podcast episodes? What are the most interesting, innovative, or unexpected ways that you have seen Shortwave used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Shortwave? When is Shortwave the wrong choice? What do you have planned for the future of Shortwave?
Contact Info
LinkedIn Blog
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with mach
GenAI can help data engineers become more productive, and data engineering can help GenAI drive new levels of innovation. Published at: https://www.eckerson.com/articles/achieving-fusion-how-genai-and-data-engineering-help-one-another
Last year saw the proliferation of countless AI tools and initiatives, many companies looked to find ways where AI could be leveraged to reduce operational costs and pressure wherever possible. 2023 was a year of experimentation for anyone trying to harness AI, but we can’t walk forever. To keep up with the rapidly changing landscape in business, last year’s experiments with AI need to find their feet and allow us to run. But how do we know which initiatives are worth fully investing in? Will your company culture impede the change management that is necessary to fully adopt AI? Sanjay Srivastava is the Chief Digital Strategist at Genpact. He works exclusively with Genpact’s senior client executives and ecosystem technology leaders to mobilize digital transformation at the intersection of cutting-edge technology, data strategy, operating models, and process design. In his previous role as Chief Digital Officer at Genpact, Sanjay built out the company’s offerings in artificial intelligence, data and analytics, automation, and digital technology services. He leads Genpact’s artificial-intelligence-enabled platform that delivers industry-leading governance, integration, and orchestration capabilities across digital transformations. Before joining Genpact, Sanjay was a Silicon Valley serial entrepreneur and built four high-tech startups, each of which was successfully acquired by Akamai, BMC, FIS, and Genpact, respectively. Sanjay also held operating leadership roles at Hewlett Packard, Akamai, and SunGard (now FIS), where he oversaw product management, global sales, engineering, and services businesses. In the episode, Sanjay and Richie cover the shift from experimentation to production seen in the AI space over the past 12 months, the importance of corporate culture in the adoption of AI in a business environment, how AI automation is revolutionizing business processes at GENPACT, how change management contributes to how we leverage AI tools at work, adapting skill development pathways to make the most out of AI, how AI implementation changes depending on the size of your organization, future opportunities for AI to change industries and much more. Links Mentioned in the Show: Genpact[Course] Implementing AI Solutions in BusinessArticle: AI adoption accelerates as enterprise PoCs show productivity gainsRelated Episode: How Generative AI is Changing Business and Society with Bernard Marr, AI Advisor, Best-Selling Author, and FuturistRewatch sessions from RADAR: The Analytics 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
Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style!
In this episode, titled, "#46 Debunking Devon, Exploring RAG Frameworks, and Tech for a Better World", our special guest Martin Van Mollekot adds a rich layer of insight to our tech stew, covering everything from 3D-printed humanoids to the harmonious blend of AI and music, all while exploring how tech is cultivating a better world. 3D Printing: Martin discusses building a humanoid using resources from Thingiverse.AI Generated Music: Exploring Udio, an AI that not only composes music but adds vocals to match your taste.Devin Debunked: Unpacking the claims of the "First AI Software Engineer" and why it's not quite time to worry about AI taking coding jobs.GPT-4 Over Humans? A critical look at whether AI could replace junior analysts in the current tech landscape.The Data Science Dilemma: Is Data Science Dead? Discussing the evolution and future relevance of data science, with Zapier highlighted for its accessible toolset.RAG Frameworks Galore:. Discover the evolving buffet of RAG frameworks, making data handling smoother – and whether they're up to the hype: Ragflow, Pine Cone, Verba, and R2R. Tech for a Better World: Martin shares his personal story of how computer vision technology can aid farmers in managing their livestock.Hip-Hop and Generative AI: How generative AI is stirring up the music industry & tips from Bart on reproducing hit tracks.The Low-Code Revolution: Martin shares his insights on the rise of low-code/no-code platforms in data management.
Send us a text Toby Coulthard, CPO at Phrasee talks to Marketing Tech as the interface between human psychology and technology. Phrasee is a marketing platform that leverages generative AI for natural language generation, optimizing content at scale for enterprise clients such as Walgreens, Macy’s, Currys, and more. 01:21 Meet Toby Coulthard 05:46 MarTech06:43 Chief Product Officer at Phrasee10:02 LLMs in MarTech10:56 Tools at Phrasee12:21 Client References 17:00 The Marketing Journey 20:50 What Model Then???22:22 "Winning a Campaign"29:54 Phrasee Differentiation32:43 Marketing of the Future39:29 For FunLinkedIn: https://www.linkedin.com/in/toocou/ Website: https://phrasee.co/ Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
There aren’t many retail giants like Walmart. In fact, there are none. The multinational generates 650bn in revenue, (including 50bn in eCommerce)—the highest revenue of any retailer globally. With over 10,000 stores worldwide and a constantly evolving product line, Walmart’s data & AI function has a lot to contend with when it comes to customer experience, demand forecasting, supply chain optimization and where to use AI effectively. So how do they do it? What can we learn from one of the most successful and well-known organizations on the planet? Swati Kirti is a Senior Director of Data Science, leading the AI/ML charter for Walmart Global Tech’s international business in Canada, Mexico, Central America, Chile, China, and South Africa. She is responsible for building AI/ML models and products to enable automation and data-driven decisions, powering superior customer experience and realizing value for omnichannel international businesses across e-commerce, stores, supply chain, and merchandising. In the episode, Swati and Richie explore the role of data and AI at Walmart, how the data and AI teams operate under Swati’s supervision, how Walmart improves customer experience through the use of data, supply chain optimization, demand forecasting, retail-specific data challenges, scaling AI solutions, innovation in retail through AI and much more. Links Mentioned in the Show: Article - Walmart’s Generative AI search puts more time back in customers' handsWalmart Global Tech[Course] Implementing AI Solutions in BusinessRelated Episode: How Generative AI is Changing Business and Society with Bernard Marr, AI Advisor, Best-Selling Author, and FuturistRewatch sessions from RADAR: The Analytics 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
If you're ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about. Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more accessible and genuinely engaging. Each brief, self-contained chapter journeys through a fundamental question in AI, unraveling it with clear explanations, diagrams, and hands-on exercises. WHAT'S INSIDE: FOCUSED CHAPTERS: Key questions in AI are answered concisely, and complex ideas are broken down into easily digestible parts. WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network architectures and model evaluation to computer vision and natural language processing. PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance, fine-tuning large models, and more. You'll also explore how to: Manage the various sources of randomness in neural network training Differentiate between encoder and decoder architectures in large language models Reduce overfitting through data and model modifications Construct confidence intervals for classifiers and optimize models with limited labeled data Choose between different multi-GPU training paradigms and different types of generative AI models Understand performance metrics for natural language processing Make sense of the inductive biases in vision transformers If you've been on the hunt for the perfect resource to elevate your understanding of machine learning, Machine Learning Q and AI will make it easy for you to painlessly advance your knowledge beyond the basics.