Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Professional Workflows⚡️ Learn how to build every workflow in Bubble, and gain further insight into using each workflow in production environments. Open to all skill levels with basic how-tos mixed with advanced concepts🔥 Pre-order sale ending soon:

14,586 Aufrufe • vor 3 Jahren •via X (Twitter)

7 Kommentare

Profilbild von Build with Adi 💯
Build with Adi 💯vor 3 Jahren

@bubble This is gonna be fun!

Profilbild von Josh Munsch
Josh Munschvor 3 Jahren

@bubble Just purchased databases and the presale for workflows :) ready to learn 🤘

Profilbild von JJ Englert
JJ Englertvor 3 Jahren

@bubble 🙌🏽🙌🏽 let’s do it!

Profilbild von Brian Friedman
Brian Friedmanvor 3 Jahren

@bubble Signed up. Can’t wait

Profilbild von JJ Englert
JJ Englertvor 3 Jahren

@bubble Appreciate your support!!

Profilbild von NoCode Databases
NoCode Databasesvor 3 Jahren

@bubble 🔥🔥

Profilbild von AntiNews
AntiNewsvor 3 Jahren

@bubble Looks like a great course!

Ähnliche Videos

Learn to build and deploy GenAI pipelines in "Orchestrating Workflows for GenAI Applications", built in partnership with Astronomer and taught by Kenten Danas, the company's DevRel Senior Manager, and Tamara Fingerlin, developer advocate. Many GenAI applications require executing a pipeline comprising many steps. For example, a RAG app for recommending books might ingest and embed book descriptions, store the embeddings in a vector database, and later use the database to retrieve and recommend specific books based on a user query. After having prototyped this -- maybe in a Jupyter notebook -- how do you turn this into a reliable, repeatable workflow to run in production? In this short course, you’ll learn to build reliable GenAI pipelines and orchestrate them using the popular open-source tool Airflow 3.0. You’ll learn to break down a workflow into discrete tasks so that an orchestration framework can schedule tasks to run in the right order at the right time (using time-based or data-aware triggers), and execute tasks in parallel when possible. It can also use retries to recover gracefully from failure (such as transient API rate limits) and provide observability (using Airflow UI) to help you track the status of the pipeline. You'll do this by using Airflow dags, which helps sequence tasks that need to run in a specific order, with clear task dependencies. By the end of this course, you’ll know how to turn your prototype Jupyter notebook or Python script into production-ready workflow. Please sign up here:

Andrew Ng

73,584 Aufrufe • vor 1 Jahr