Inspiring ideas or useful tools to build projects
ML in Production - Deployment Series
A multi-part blog series on deploying machine learning models in an automated, reproducible, and auditable manner.
Top Down Introduction to BERT with HuggingFace and PyTorch
I will also provide some intuition into how BERT works with a top down approach (applications to algorithm).
MLOps Tutorial Series
How to create an automatic model training & testing setup using GitHub Actions and Continuous Machine Learning (CML).
Model Serving using FastAPI and Streamlit
Simple example of usage of streamlit and FastAPI for ML model serving.
NLP for Developers: Shrinking Transformers | Rasa
In this video, Rasa Senior Developer Advocate Rachael will talk about different approaches to make transformer models smaller.
Build your first data warehouse with Airflow on GCP
What are the steps in building a data warehouse? What cloud technology should you use? How to use Airflow to orchestrate your pipeline?
Rich repository of ML drawings exported to XML and PNG created using draw.io
Machine Learning: Tests and Production
Best practices for testing ML-based systems.
Simple Transformers: Transformers Made Easy
Simple Transformers removes complexity and lets you get down to what matters – model training and experimenting with the Transformer model architectures.
How to Set Up a HTML App with FastAPI, Jinja, Forms & Templates
I couldn’t find any guides on how to serve HTML with FastAPI. Thus, I wrote this simple article to plug the hole on the internet.
1 - 10
How to add Projects to your Collection
Identify the project you want to add to this collection. You can discover projects on the
page or search for them using the search bar at the top left of any page.
Once you've identified the project, click on the green bookmark symbol to its right.
means that you've already added it to some Collections and
means that you have yet to add it to any Collections.
Select the Collections you want to add the project to and unselect the Collections you want to remove the project from (if the project already existed in one of your collections).
and continue to add more projects and create more Collections.
Don't forget to tag
in your comment, otherwise they may not be notified.
Data scientist/software engineer, machine learning practitioner
Share this collection
Share what you've made with ML.