Streamlit for ML #2 - ML Models and APIs

▶️ Streamlit for ML Part 3: https://www.youtube.com/watch?v=lYDiSCDcxmc&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=3

Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in production environments by ever-larger organizations.

All of this means that there is no better time to pick up some experience with Streamlit. Fortunately, the basics of Streamlit are incredibly easy to learn, and for most tools, this will be more than you need!

In this series, we will introduce Streamlit by building a general knowledge Q&A interface. We will learn about key Streamlit components like write, text_input, container. How to use external libraries like Bootstrap to quickly create new app components. And use caching to speed up our app.

🔗 Code to Create Index: https://gist.github.com/jamescalam/2123ce0bb8a871f48a151a023a7ece67

📕 Article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec

🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5

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📖 Friend link to article: https://towardsdatascience.com/getting-started-with-streamlit-for-nlp-75fe463821ec?sk=ac5e0b7c39938f52162862411a66a58b

👾 Discord: https://discord.gg/c5QtDB9RAP

00:00 Intro 00:47 Creating the Vector DB 08:56 Implementing Retrieval ... https://www.youtube.com/watch?v=U0EoaFFGyTg