mlops-zoomcamp VS data-engineering-zoomcamp

There is MLOps Zoomcamp course (which shows end-to-end MLOps process with open-source MLOps tools) https://github.com/DataTalksClub/mlops-zoomcamp.

Yes, your point exact. I haven't found any quality ones to be honest. There is the coursera mlops course (i did complete this one) which covers tfx (and other services like kubeflow but only scratching the surface) but that seems to be an overkill for me and also very dependant on tensorflow. I have came across this lately https://github.com/DataTalksClub/mlops-zoomcamp (havent went through it in detail) which seems to cover a lot but still missing both the full picture example and i dont see e.g. A/B testing, canary there

datatalksclub https://github.com/DataTalksClub/mlops-zoomcamp

DataTalksClub/mlops-zoomcamp: Free MLOps course from DataTalks.Club (github.com)

I found this one and also online events hosted by the MLOps community.

I think this course is a great one for practical MLOps. They have video lectures with homework https://github.com/DataTalksClub/mlops-zoomcamp

This is one of the possible resources online https://github.com/DataTalksClub/mlops-zoomcamp. It's a free course. And I also know that the MLOps community hosts many events around the topic.

I had seen a GitHub which uses like popular open source projects to build an end to end ml lifecycle. Im planning to follow it. https://github.com/DataTalksClub/mlops-zoomcamp

Maybe this will help https://github.com/DataTalksClub/mlops-zoomcamp

References: Data engineering zoomcamp week 6 course and homework notes: https://github.com/DataTalksClub/data-engineering-zoomcamp/tree/main/cohorts/2024/06-streaming

dbt is the main part of my data engineering project for Data Talks Club's data engineering zoomcamp. After a few frustrating errors on my part, I finally figured out how to make models, where to put the staging models and where to put the core models, how to compile a seed file, and how to join it to the main file in order to produce data for visualization. I also used the git interface to continually upgrade my repository. This was extremely convenient and helpful.

For Week 4 of DataTalksClub's data engineering zoomcamp, we had to install dbt and create a project. This was a formidable task. dbt is a data transformation tool that enables data analysts and engineers to transform data in a cloud analytics warehouse, BigQuery in our case. It took me a very long time to do this, and in this case I needed the homework extension.

In this video we learned how to test and document dbt models. We also learned about the codegen library. This is part of Week 4 of the data engineering zoomcamp by DataTalksClub.

If you want to run these commands yourself, either in a Jupyter notebook or in Google Colab, you can get the file from HERE. You can get an overview of the workshop HERE. When I ran in a Jupyter notebook, I had to delete the first line (%%capture) and put quotes around dlt[duckdb] in the second line.

Take a look.DE zoomcamp

If you need guidance you can attend the data engineering zoomcamp, it's free and quite solid.

Welcome. Start with Data Engineering Zoomcamp, try and build a project, see if you like it, then continue to get into deeper resources.

Can take a look at this - https://github.com/DataTalksClub/data-engineering-zoomcamp

I think you can start with something free with this pretty practical course on Data Engineering from DataTalksClub - https://github.com/DataTalksClub/data-engineering-zoomcamp