ML Workflow

ML Problem Definition

Introduction to Machine Learning Problem Framing, Google

https://developers.google.com/machine-learning/problem-framing/formulate

Formulating the Problem, Amazon

https://docs.aws.amazon.com/machine-learning/latest/dg/formulating-the-problem.html

How to Define Your Machine Learning Problem

https://machinelearningmastery.com/how-to-define-your-machine-learning-problem/

Facebook Field Guide to Machine Learning

https://research.facebook.com/blog/2018/05/the-facebook-field-guide-to-machine-learning-video-series/

Machine learning workflow, Google

https://cloud.google.com/ai-platform/docs/ml-solutions-overview

Overview of the End-to-End Machine Learning Workflow

https://ml-ops.org/content/end-to-end-ml-workflow

Hands on ML with Scikit-learn

Early release version on Kaggle

Build end-to-end machine learning workflows with Amazon SageMaker and Apache Airflow

https://aws.amazon.com/blogs/machine-learning/build-end-to-end-machine-learning-workflows-with-amazon-sagemaker-and-apache-airflow/

Vertex AI, Google

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

https://cloud.google.com/vertex-ai#section-1