DataFramed DataCamp

Machine learning models are often thought to be mainly utilized by large tech companies that run large and powerful models to accomplish a wide array of tasks. However, machine learning models are finding an increasing presence in edge devices such as smart watches.
ML engineers are learning how to compress models and fit them into smaller and smaller devices while retaining accuracy, effectiveness, and efficiency. The goal is to empower domain experts in any industry around the world to effectively use machine learning models without having to become experts in the field themselves.
Daniel Situnayake is the Founding TinyML Engineer and Head of Machine Learning at Edge Impulse, a leading development platform for embedded machine learning used by over 3,000 enterprises across more than 85,000 ML projects globally. Dan has over 10 years of experience as a software engineer, which includes companies like Google (where he worked on TensorFlow Lite) and Loopt, and co-founded Tiny Farms America’s first insect farming technology company. He wrote the book, "TinyML," and the forthcoming "AI at the Edge".
Daniel joins the show to talk about his work with EdgeML, the biggest challenges facing the field of embedded machine learning, the potential use cases of machine learning models in edge devices, and the best tips for aspiring machine learning engineers and data science practitioners to get started with embedded machine learning.

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.

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.

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.

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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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.