Machine Learning Episodes

Machine Learning Articles

Machine Learning Tutorial

Machine Learning Cheat Sheets

Adel and Eric explore the reasons why machine learning projects don't make it into production, the BizML Framework or how to bring business stakeholders into the room when building machine learning use cases, what the previous machine learning hype cycle can teach us about generative AI and a lot more. 


Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling Author

Discover principles for great ML product design, data collection and packaging outputs into slick user design. 

Building Great Machine Learning Products at Opendoor

Daniel Situnayake talks about his work with EdgeML, the biggest challenges in embedded machine learning, potential use cases of machine learning models in edge devices, and the best tips for aspiring machine learning engineers and data science practices.

Embedded Machine Learning on Edge Devices

https://www.datacamp.com/blog/embedded-machine-learning-on-edge-devices

Serg Masis talks about the different challenges affecting model interpretability in machine learning, how bias can produce harmful outcomes in machine learning systems and the different types of technical and non-technical solutions to tackling bias. 

Interpretable Machine Learning 

https://www.datacamp.com/blog/interpretable-machine-learning

In this episode of DataFramed, we speak with Noah Gift, founder of Pragmatic AI Labs and prolific author about operationalizing machine learning in organizations and his new book Practical MLOPs.

Scaling Machine Learning Adoption: A Pragmatic Approach

https://www.datacamp.com/blog/scaling-machine-learning-adoption-a-pragmatic-approach

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs, an AI Observability company on a mission to build the interface between AI and human operators.

Operationalizing Machine Learning with MLOps

https://www.datacamp.com/blog/operationalizing-machine-learning-with-mlops

Cathy and Hugo discuss the current lack of fairness in artificial intelligence, how societal biases are perpetuated by algorithms and how both transparency and auditability of algorithms will be necessary for a fairer future.

Weapons of Math Destruction

https://www.datacamp.com/blog/cathy-oneil-discusses-the-current-lack-of-fairness-in-artificial-intelligence-and-much-more

What is the role of data science in product development at github, what does it means to use computation to build products to solve real-life decision making, practical challenges and what does building data products at github actua

Machine Learning & Data Science at Github

https://www.datacamp.com/blog/machine-learning-and-data-science-at-github-transcript

Will the future of data science and machine learning be automated entirely? Which verticals will experience the largest disruption? What will the role of data science become?

Automated Machine Learning

The modern superpower of deep learning and where it has the largest impact, past, present and future, filtered through the lens of Michelle Gill's work at NVIDIA.

Deep Learning at NVIDIA

How can data science and deep learning be leveraged to detect fake news? Come on a journey with Mike Tamir, Head of Data Science at Uber ATG, who is building out a data science product that classifies text as news, editorial, satire, hate speech and fake news.