From May 8-11, discover expert insights from four industry leaders from OpenAI, Accenture, Kubrick Group, and Kanayma LLC on how to navigate the era of AI.

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

Data leaders play a critical role in driving innovation and growth in various industries, and this is particularly true in highly regulated industries such as aviation. In such industries, data leaders face unique challenges and opportunities, working to balance the need for innovation with strict regulatory requirements. This week’s guest is Derek Cedillo, who has 27 years of experience working in Data and Analytics at GE Aerospace. Derek currently works as a Senior Manager for GE Aerospace’s Remote Monitoring and Diagnostics division, having previously worked as the Senior Director for Data Science and Analytics. In the episode, Derek shares the key components to successfully managing a Data Science program within a large and highly regulated organization. He also shares his insights on how to standardize data science planning across various projects and how to get a Data Scientists to think and work in an agile manner. We hear about ideal data team structures, how to approach hiring, and what skills to look for in new hires.  The conversation also touches on what responsibility Data Leaders have within organizations, championing data-driven decisions and strategy, as well as the complexity Data Leaders face in highly regulated industries. When it comes to solving problems that provide value for the business, engagement and transparency are key aspects. Derek shares how to ensure that expectations are met through clear and frank conversations with executives that try to align expectations between management and Data Science teams. 

Finally, you'll learn about validation frameworks, best practices for teams in less regulated industries, what trends to look out for in 2023 and how ChatGPT is changing how executives define their expectations from Data Science teams. 

Links to mentioned in the show: The Checklist Manifesto by Atul Gawande Team of Teams by General Stanley McChrystal The Harvard Data Science Review Podcast

Relevant Links from DataCamp: Article: Storytelling for More Impactful Data Science Course: Data Communication Concepts Course: Data-Driven Decision-Making for Business

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.

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.

In 2020, OpenAI launched GPT-3, a large language AI model that is demonstrating the potential to radically change how we interact with software, and open up a completely new paradigm for cognitive software applications.

Today’s episode features Sandra Kublik and Shubham Saboo, authors of GPT-3: Building Innovative NLP Products Using Large Language Models. We discuss what makes GPT-3 unique, transformative use-cases it has ushered in, the technology powering GPT-3, its risks and limitations, whether scaling models is the path to “Artificial General Intelligence”, and more.

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