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Ryan Dolley and I chat about why BI needs to evolve, moving beyond dashboards, the impact of generative AI on analytics, SuperDataBros, and more.
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The concept of literate programming, or the idea of programming in a document, was first introduced in 1984 by Donald Knuth. And as of today, notebooks are now the defacto tool for doing data science work. So as the data tooling space continues to evolve at breakneck speed, what are the possible directions the data science notebook can take? In this episode of DataFramed, we talk with Dr. Jodie Burchell, Data Science Developer Advocate at JetBrains, to find out how data science notebooks evolved into what they are today, what her predictions are for the future of notebooks and data science, and how generative AI will impact data teams going forward. Jodie completed a Ph.D. in clinical psychology and a postdoc in biostatistics before transitioning into data science. She has since worked for 7 years as a data scientist, developing products ranging from recommendation systems to audience profiling. She is also a prolific content creator in the data science community. Throughout the episode, Jodie discusses the evolution of data science notebooks over the last few years, noting how the move to remote-based notebooks has allowed for the seamless development of more complex models straight from the notebook environment. Jodie and Adel’s conversation also covers tooling challenges that have led to modern IDEs and notebooks, with Jodie highlighting the importance of good database tooling and visibility. She shares how data science notebooks have evolved to help democratize data for the wider organization, the tradeoffs between engineering-led approaches to tooling compared to data science approaches, what generative AI means for the data profession, her predictions for data science, and more. Tune in to this episode to learn more about the evolution of data science notebooks and the challenges and opportunities facing the data science community today. Links to mentioned in the show: DataCamp Workspace: An-in Browser Notebook IDEJetBrains' DataloreNick Cave on ChatGPT song lyrics imitating his styleGitHub Copilot More on the topic: The Past, Present, And Future of The Data Science NotebookHow to Use Jupyter Notebooks: The Ultimate Guide
Summary
The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack today to learn more Your host is Tobias Macey and today I'm interviewing Matt Turck about his annual report on the Machine Learning, AI, & Data landscape and the insights around data infrastructure that he has gained in the process
Interview
Introduction How did you get involved in the area of data management? Can you describe what the MAD landscape report is and the story behind it?
At a high level, what is your goal in the compilation and maintenance of your landscape document? What are your guidelines for what to include in the landscape?
As the data landscape matures, how have you seen that influence the types of projects/companies that are founded?
What are the product categories that were only viable when capital was plentiful and easy to obtain? What are the product categories that you think will be swallowed by adjacent concerns, and which are likely to consolidate to remain competitive?
The rapid growth and proliferation of data tools helped establish the "Modern Data Stack" as a de-facto architectural paradigm. As we move into this phase of contraction, what are your predictions for how the "Modern Data Stack" will evolve?
Is there a different architectural paradigm that you see as growing to take its place?
How has your presentation and the types of information that you collate in the MAD landscape evolved since you first started it?~~ What are the most interesting, innovative, or unexpected product and positioning approaches that you have seen while tracking data infrastructure as a VC and maintainer of the MAD landscape? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the MAD landscape over the years? What do you have planned for future iterations of the MAD landscape?
Contact Info
Website @mattturck on Twitter MAD Landscape Comments Email
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 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. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
MAD Landscape First Mark Capital Bayesian Learning AI Winter Databricks Cloud Native Landscape LUMA Scape Hadoop Ecosystem Modern Data Stack Reverse ETL Generative AI dbt Transform
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Send us a text Datatopics is a podcast presented by Kevin Missoorten to talk about the fuzzy and misunderstood concepts in the world of data, analytics, and AI and get to the bottom of things.
We are back with a new episode, exploring the fascinating and rapidly evolving field of generative AI. In this episode, your host Kevin is joined by three experts in the field: Murilo, Tim, and Vitale, to discuss the future of generative AI and its potential impact on our world. The conversation delves into the ethical considerations, the challenges that need to be overcome for it to reach its full potential, and the impact it will have on people's lives. Kevin and his guests also explore the exciting possibilities that generative AI presents, such as its potential to transform the way we work and live our lives. Join Kevin, Murilo, Tim, and Vitale for a thought-provoking discussion about the future of generative AI and its implications for society.
Datatopics is brought to you by Dataroots Music: The Gentlemen - DivKidThe thumbnail is generated by Midjourney
Sarah and Chris are both at the forefront of bringing the promise of gen AI to our actual work as data people—which is a unique challenge! Precise truth is critical for business questions in a way that it's not for a consumer search query. Sarah Nagy is the CEO of Seek AI, a startup that aims to use natural language processing to change how professionals work with data. Chris Aberger currently leads Numbers Station AI, a startup focused on data-intensive workflow automation. In this conversation with Tristan and Julia, they dive into what this future might actually look like, and tangibly what we can expect from gen AI in the short/medium term. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.
Throughout 2022, there was an explosion in generative AI for images and text. GPT-3, DALLE-2, pointed us towards an AI-driven future. Recently, ChatGPT has taken the (data) world by storm — prompting many questions over how generative AI can be used in day to day activities. With the incredible amount of hype surrounding these new tools, we wanted to have a discussion grounded in how these tools are being operationalized today. Enter Scott Downes. Scott is the CTO of Invisible Technologies, a process automation platform that uses GPT-3 and other generative text technologies. Scott joins the show to talk about how organizations and data professionals can maximize the potential of these tools and how AI and humans can work together in a complementary fashion to optimize workflows, reduce time-intensive, tedious tasks, and do higher quality work. Scott has a decade of experience in technology, product engineering, and technical leadership, making a veteran in training and mentoring employees across the organization, whether their roles are more creative or more technical. Throughout the conversation, we talk about what Invisible Technologies uses GPT-3 to optimize workflows, a brief overview of GPT-3 and its use cases for working with text, how GPT-3 helps companies scale their operations, the promises of tools ChatGPT, how AI analysis and human review can work together to save lives, and much more.
In 2022, we saw significant developments in the field of data. From the emergence of generative AI to the growth of low-code data tools and AI assistants—these advancements signal an upcoming paradigm shift, where data-powered tools and machine learning systems will radically transform workflows across various professions. 2022 also saw digital transformation remain a major theme for organizations across industries as they sought to embrace new ways of working, reaching customers, and providing value. As 2023’s looming economic uncertainty puts pressure on organizations to maximize ROI from their investments, digital and data transformation will continue to be one of the key levers by which organizations can cut costs and scale value for their stakeholders. So we’ve invited DataCamp’s co-founders, CEO Jonathan Cornelissen and COO Martijn Theuwissen to break down the top data trends they are seeing in the data space today, as well as their predictions for the future of the data industry. Jonathan Cornelissen is the CEO and co-founder of DataCamp. As the CEO of DataCamp, he helped grow DataCamp to upskill over 10M+ learners and 2800+ teams and enterprise clients. He is interested in everything related to data science, education and entrepreneurship. He holds a PhD in financial econometrics, and was the original author of an R package for quantitative finance.
Martijn Theuwissen is the COO and co-founder of DataCamp. As the COO of DataCamp, he helps DataCamp’s enterprise clients on their data and digital transformation strategies, enabling them to make the most of DataCamp for Business’s offering, and helping them transform how their workforce uses data.
2022 was an incredible year for Generative AI. From text generation models like GPT-3 to the rising popularity of AI image generation tools, generative AI has rapidly evolved over the last few years in both its popularity and its use cases.
Martin Musiol joins the show this week to explore the business use cases of generative AI, and how it will continue to impact the way the society interacts with data. Martin is a Data Science Manager at IBM, as well as Co-Founder and an instructor at Generative AI, teaching people to develop their own AI that generates images, videos, music, text and other data. Martin has also been a keynote speaker at various events, such as Codemotion Milan. Having discovered his passion for AI in 2012, Martin has turned that passion into his expertise, becoming a thought leader in AI and machine learning space.
In this episode, we talk about the state of generative AI today, privacy and intellectual property concerns, the strongest use cases for generative AI, what the future holds, and much more.
Last year, the film development and production company End Cue produced a short film, called Sunspring, that was entirely written by an artificial intelligence using neural networks. More specifically, it was authored by a recurrent neural network (RNN) called long short-term memory (LSTM). According to End Cue's Chief Technical Officer, Deb Ray, the company has come a long way in improving the generative AI aspect of the bot. In this episode, Deb Ray joins host Kyle Polich to discuss how generative AI models are being applied in creative processes, such as screenwriting. Their discussion also explores how data science for analyzing development projects, such as financing and selecting scripts, as well as optimizing the content production process.
Curious about the future of media creation? Explore the latest announcements and capabilities for Imagen, Veo, and Lyria, Google’s suite of generative AI media tools. Witness demonstrations of new features and discover how they can revolutionize your creative workflows.
Leverage Google Cloud Observability when instrumenting your code with logging, monitoring, and tracing capabilities. Try it out yourself in one of the popular programming languages, such as Java, Python, JavaScript, or Go.
Join experts from Box, Typeface, Glean, CitiBank, and Securiti AI for actionable tips to effectively implement AI-powered apps across your enterprise. We'll discuss real use cases, implementation best practices, and how to measure returns on investment — whether that's in marketing, financial services, HR, or beyond.
Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.
Use generative AI to suggest gardening products to customers based on their specific needs. We use Genkit to help users determine what’s wrong with their house plants and which gardening products could help revive those plants.
Learn how generative AI and Firebase products can help shoppers find exactly what they are looking for in store – like at their local grocer. Genkit generates recipes and Data Connect helps locate items on shelves, all hosted on Firebase App Hosting.
Did you know? You only need One API to Rule Them All! Google's Gen AI SDK provides a simple path to both the Gemini API and Vertex AI!
As Satya Nadella unveils the next steps in integrating AI agents across business functions, Kathleen Mitford, CVP of Industry Marketing, and Satish Thomas, CVP of Business and Industry Solutions Engineering, explore how Microsoft Cloud for Industry enables customers and partners to build, adapt, deploy, and manage solutions tailored to each industry’s unique needs. Focusing on Copilot and Generative AI, this broadcast will highlight AI’s transformative impact on productivity, efficiency, and innovation across industries. By combining the power of the Microsoft Cloud, with industry-specific AI insights and capabilities, and the expertise of a robust partner ecosystem, Microsoft’s approach unlocks AI’s full potential, driving significant outcomes for every industry.
Building an assistant capable of answering complex, company-specific questions and executing workflows requires first building a powerful Retrieval Augmented Generation (RAG) system. Founding engineer Eddie Zhou explains how Glean built its RAG system on Google Cloud— combining a domain-adapted search engine with dynamic prompts to harness the full capabilities of Gemini's reasoning engine. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.
Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.
In this powerful demo, learn how NTT DATA’s Agentic AI Services for Microsoft get you out of “pilot mode” and drive real business value from your agentic AI and gen AI projects Experience the industrialized framework in action-- moving you from AI experimentation to production quickly and unlock measurable return on investment. Build a crucial link between your team, the new AI agents, your existing knowledge base, and automation to drive a scalable, sustainable solution that delivers results.
Build and run predictive models using SQL in BigQuery. Leverage the latest models for generative AI tasks.