Talk Python Training

Open Sourcing The Anvil Full Stack Python Web App Platform - Episode 291

Building a complete web application requires expertise in a wide range of disciplines. As a result it is often the work of a whole team of engineers to get a new project from idea to production. Meredydd Luff and his co-founder built the Anvil platform to make it possible to build full stack applications entirely in Python. In this episode he explains why they released the application server as open source, how you can use it to run your own projects for free, and why developer tooling is the sweet spot for an open source business model. He also shares his vision for how the end-to-end experience of building for the web should look, and some of the innovative projects and companies that were made possible by the reduced friction that the Anvil platform provides. Give it a listen today to gain some perspective on what it could be like to build a web app.

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Scale Your Data Science Teams With Machine Learning Operations Principles - Episode 289

Building a machine learning model is a process that requires well curated and cleaned data and a lot of experimentation. Doing it repeatably and at scale with a team requires a way to share your discoveries with your teammates. This has led to a new set of operational ML platforms. In this episode Michael Del Balso shares the lessons that he learned from building the platform at Uber for putting machine learning into production. He also explains how the feature store is becoming the core abstraction for data teams to collaborate on building machine learning models. If you are struggling to get your models into production, or scale your data science throughput, then this interview is worth a listen.

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Bringing Artificial Intelligence Projects From Idea To Production - Episode 287

Artificial intelligence applications can provide dramatic benefits to a business, but only if you can bring them from idea to production. Henrik Landgren was behind the original efforts at Spotify to leverage data for new product features, and in his current role he works on an AI system to evaluate new businesses to invest in. In this episode he shares advice on how to identify opportunities for leveraging AI to improve your business, the capabilities necessary to enable aa successful project, and some of the pitfalls to watch out for. If you are curious about how to get started with AI, or what to consider as you build a project, then this is definitely worth a listen.

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