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Full Stack Deep Learning
Full Stack Deep Learning helps you bridge the gap from training machine learning models to deploying AI systems in the real world.
production full-stack deep-learning course
GitHub Actions & Machine Learning Workflows with Hamel Husain
In this talk, Hamel will provide a brief tutorial on GitHub Actions, and will show you how you can use this new tool to automate your ML workflows.
github-actions machine-learning workflows video
Omega|ml - building and deploying ML models the easy way
Deploying ML is hard. It should not be. omega|ml makes it a breeze.
mlops production code article
What I Learned From Looking at 200 Machine Learning Tools
To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find.
production machine-learning mlops survey
Getting started with large-scale ETL jobs using Dask and AWS EMR
EMR is AWS’s distributed data platform, which we can interact with and submit jobs to from a JupyterLab notebook running on our local machine.
exploratory-data-analysis dask aws notebook
GitHub Actions for Machine Learning
This presentation discusses the use of GitHub Actions to automate certain steps of a toy ML project.
github mlops scikit-learn wandb
Unpopular Opinion - Data Scientists Should Be More End-to-End
I believe data scientists can be more effective by being end-to-end.
full-stack career article mlops
Data Science Meets Devops: MLOps with Jupyter, Git, & Kubernetes
An end-to-end example of deploying a machine learning product using Jupyter, Papermill, Tekton, GitOps and Kubeflow.
production end-to-end mlops gitops
How to Set Up Continuous Integration for Machine Learning
How to Set Up Continuous Integration for Machine Learning with Github Actions and Neptune: Step by Step Guide.
ci-cd deep-learning experiment-tracking code
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