100 episodes

DataFramed is a podcast for data & analytics leaders looking to scale data science throughout an organization by equipping them with the insights to drive value from data science and create a data-driven culture. Each episode will feature a conversation with various data science and analytics leaders who are transforming their organizations and are at the forefront of the data revolution. Whether you’re just getting started in your data career, or you’re a data leader looking to scale data-driven decisions in your organization, you’ve found the right community. Welcome to DataFramed!

DataFramed DataCamp

    • Technology
    • 5.0 • 2 Ratings

DataFramed is a podcast for data & analytics leaders looking to scale data science throughout an organization by equipping them with the insights to drive value from data science and create a data-driven culture. Each episode will feature a conversation with various data science and analytics leaders who are transforming their organizations and are at the forefront of the data revolution. Whether you’re just getting started in your data career, or you’re a data leader looking to scale data-driven decisions in your organization, you’ve found the right community. Welcome to DataFramed!

    #99 Post-Deployment Data Science

    #99 Post-Deployment Data Science

    Many machine learning practitioners dedicate most of their attention to creating and deploying models that solve business problems. However, what happens post-deployment? And how should data teams go about monitoring models in production?
    Hakim Elakhrass is the Co-Founder and CEO of NannyML, an open-source python library that allows users to estimate post-deployment model performance, detect data drift, and link data drift alerts back to model performance changes. Originally, Hakim started a machine learning consultancy with his NannyML co-founders, and the need for monitoring quickly arose, leading to the development of NannyML.
    Hakim joins the show to discuss post-deployment data science, the real-world use cases for tools like NannyML, the potentially catastrophic effects of unmonitored models in production, the most important skills for modern data scientists to cultivate, and more.

    • 33 min
    #98 Interpretable Machine Learning

    #98 Interpretable Machine Learning

    One of the biggest challenges facing the adoption of machine learning and AI in Data Science is understanding, interpreting, and explaining models and their outcomes to produce higher certainty, accountability, and fairness.
    Serg Masis is a Climate & Agronomic Data Scientist at Syngenta and the author of the book, Interpretable Machine Learning with Python. For the last two decades, Serg has been at the confluence of the internet, application development, and analytics. Serg is a true polymath. Before his current role, he co-founded a search engine startup incubated by Harvard Innovation Labs, was the proud owner of a Bubble Tea shop, and more.
    Throughout the episode, Serg spoke about the different challenges affecting model interpretability in machine learning, how bias can produce harmful outcomes in machine learning systems, the different types of technical and non-technical solutions to tackling bias, the future of machine learning interpretability, and much more.

    • 50 min
    #97 How Salesforce Created a High-Impact Data Science Organization

    #97 How Salesforce Created a High-Impact Data Science Organization

    Anjali Samani, Director of Data Science & Data Intelligence at Salesforce, joins the show to discuss what it takes to become a mature data organization and how to build an impactful, diverse data team. As a data leader with over 15 years of experience, Anjali is an expert at assessing and deriving maximum value out of data, implementing long-term and short-term strategies that directly enable positive business outcomes, and how you can do the same.
    You will learn the hallmarks of a mature data organization, how to measure ROI on data initiatives, how Salesforce implements its data science function, and how you can utilize strong relationships to develop trust with internal stakeholders and your data team.

    • 44 min
    #96 GPT-3 and our AI-Powered Future

    #96 GPT-3 and our AI-Powered Future

    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.
    Announcement
    For the next seven days, DataCamp Premium and DataCamp for Teams are free. Gain free access by following going here. 

    • 1 hr 3 min
    #95 How to Build a Data Science Team from Scratch

    #95 How to Build a Data Science Team from Scratch

    While leading a mature data science function is a challenge in its own right, building one from scratch at an organization can be just as, if not even more, difficult. As a data leader, you need to balance short-term goals with a long-term vision, translate technical expertise into business value, and develop strong communication skills and an internalized understanding of a business's values and goals in order to earn trust with key stakeholders and build the right team.
    Elettra Damaggio is no stranger to this process. Elettra is the Director for Global Data Science at StoneX, an institutional-grade financial services network that connects clients to the global markets ecosystem. Elettra has over 10 years of experience in machine learning, AI, and various roles within digital transformation and digital business growth.
    In this episode, she shares how data leaders can balance short-term wins with long-term goals, how to earn trust with stakeholders, major challenges when launching a data science function, and advice she has for new and aspiring data practitioners.

    • 39 min
    #94 How Data Science Enables Better Decisions at Merck

    #94 How Data Science Enables Better Decisions at Merck

    In pharmaceuticals, wrong decisions can not only cost a company revenue, but they can also cost people their lives. With stakes so high, it’s vital that pharmaceutical companies have robust systems and processes in place to accurately gather, analyze, and interpret data and turn it into actionable steps to solving health issues.
    Suman Giri is the Global Head of Data Science of the Human Health Division at Merck, a biopharmaceutical research company that works to develop innovative health solutions for both people and animals. Suman joins the show today to share how Merck is using data to improve organizational decision-making, medical research outcomes, and how data science is transforming the pharmaceutical industry at scale. He also shares some of the biggest challenges facing the industry right now and what new trends are on the horizon.

    • 39 min

Customer Reviews

5.0 out of 5
2 Ratings

2 Ratings

Top Podcasts In Technology

Lex Fridman
Changelog Media
Nielsen Norman Group
The Verge
Red Hat
Laurence Bradford

You Might Also Like

Kyle Polich
Jon Krohn and Guests on Machine Learning, A.I., and Data-Career Success
Tobias Macey
Changelog Media
Michael Kennedy (@mkennedy)
Sam Charrington