podcast-study-group

Submission guidelines

Repo about podcasts making life better understood. Submission guidelines:

• include links to podcast episodes, not podcasts in general
• try to give a summary, you can cut and paste
• add supporting links
  • like this
• For the big podcasts, add a new ### sections

Data Science

Software Engineering Daily

• Sofware Engineering Daily: DataOps with Chris Bergh: Every company with a large set of customers has a large set of data–whether that company is 5 years old or 50 years old. That data is valuable whether you are an insurance company, a soft drink manufacturer, or a ridesharing company. All of these large companies know that their data is valuable, but some of them are not sure how to standardize the access patterns of that data, or build a culture around data.

Data Engineering Podcast

• Data Engineering Podcast: Defining DataOps with Chris Bergh : Managing an analytics project can be difficult due to the number of systems involved and the need to ensure that new information can be delivered quickly and reliably. That challenge can be met by adopting practices and principles from lean manufacturing and agile software development, and the cross-functional collaboration, feedback loops, and focus on automation in the DevOps movement. In this episode Christopher Bergh discusses ways that you can start adding reliability and speed to your workflow to deliver results with confidence and consistency.

This Week in Machine Learning and AI

• Building a Recommender System from Scratch at 20th Century Fox with JJ Espinoza In this talk we start out with a discussion on JJ’s transition from econometrician to data scientist, and then dig into his and his team’s experience building and deploying a content recommendation system from the ground up. In our conversation, we explore the design of a couple of key components of their system, the first of which processes movie scripts to make recommendations about which movies the studio should make, and the second processes trailers to determine which should be recommended to users. We discuss the challenges they’ve encountered fielding these systems, some of the tools that were used along the way, and a few of the upcoming projects that could be layered on top of the platform they’ve built.