We're a data science podcast, focused on the latest & greatest of the DS ecosystem, sprinkled in with our musings & data science expertise. With topics ranging from ethical AI and transparency to robot pets, our hosts, Triveni & Will, are here to keep you up to date on the latest trends, news, and big convos in data. Click hereto to listen to the Banana Data Podcast.
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In our season 2 inaugural episode, we’re debating how to approach data science pipelines (are they cyclical or linear? How should we test them?) - and how tools like Python and Kafka may not be all they’re hyped up to be in AI.
Triveni and Will sit down with Dan Shiebler, Senior ML Engineer at Twitter to tackle the final frontier of data science: production. From technical debt to model maintenance, they’ll look at what it means to have a model in production, when it's time to take a model out of production, and how challenges of technical debt can affect the entire data science pipeline.
As we near the end of the decade, Will and Triveni place their bets on the biggest data science trends for 2020- including AutoML, explainable AI, Cloud computing, and federated learning. They’ll also reflect on whether or not the trends of 2019 lived up to their hype.
Open Source software such as scikit-Learn, Python, and Spark form the backbone of data science. In a two-part series, we’re covering the ins and outs of open source - and how this special type of software supports 98% of enterprise-level companies’ data science efforts.
In part 1, we’re chatting with Andreas Mueller, a core contributor of scikit-Learn aboutthe value in open source versus corporate software, and what it looks like to run and govern this type of community-written (and driven) project.
Now that we’ve covered how open source works, we’re looking to pull back the curtain and see who’s actually contributing. In part 2/2 of our series on open source, we sat down with Reshama Shaikh, a statistician and key organizer of scikit-Learn sprints, to learn about the ups & downs of open source contributing, as well how a Sprint in Nairobi benefits Fortune 500 companies in the US.
This AI podcast has been live for two seasons - but we haven’t stepped back to ask - what even is AI? In this episode, Triveni & Will work through their definitions of AI, exploring theories, use-cases, and examples of what they think qualifies as AI - and how we measure it.
Do statistics count as AI? Does AI need to include Arnold Schwarzenegger? Who has actually achieved AI?
AI constantly promises the cutting edge. So, what’s behind the newest, hottest AI trends out there? This episode, Triveni & Will sit down with Azalia Mirhoseini, Senior Researcher at Google Brain, named on Technology Review's 35 Innovators under 35 to explore what’s really going on behind the scenes, and what’s actually overrated, underrated, and just right in the field
So many pieces of our lives are intertwined with AI - from our phones to our commutes, we’re constantly being supported by (and maybe relying on) algorithms to select our next move. On this episode, Will & Triveni take us through the many unexpected places we find AI - and challenge what it means to be a responsible AI consumer.
On this week’s episode, Karen Hao, Senior AI Reporter at the MIT Technology Review, shares what it’s like to cover AI in the peak of the hype cycle. We’ll walk through the dangers of inaccurate AI reporting, striking the delicate balance between realistic and exciting, and the what, where, and how we should be reading about AI in the news.
With Tristan Handy, CEO & Founder of Fishtown Analytics, we ask -- who should be part of the data science process? Bearing both technical requirements and business objectives, the data scientist cannot run the show on her own. We ask what it means to collaborate intra-, inter-, and out of teams, when to do bring heads together, and how to do it successfully.
In our season 2 finale, we’re asking about the business impact and ROI of data science - what are our measures of success, who calls the shots, when should we see returns, and how do we know this is all worth it?