Pages/Posts/Lectures#

Google Cloud Architecture Center [site]

  • MLOps: Continuous delivery and automation pipelines in machine learning [page] (May 18, 2023)

  • Data analytics design patterns [page] (February 6, 2023)

  • Best practices for implementing machine learning on Google Cloud [page] (December 15, 2022)

  • Intelligent Products Essentials reference architecture [page] (July 1, 2022)

  • MLOps with Intelligent Products Essentials [page] (June 28, 2022)

  • Guidelines for developing high-quality ML solutions [page] (February 17, 2022)

  • Analyzing training-serving skew with TensorFlow Data Validation [page] (March 12, 2021)

  • Architecture for MLOps using TensorFlow Extended, Vertex AI Pipelines, and Cloud Build [page]

TFX

  • Data preprocessing for ML: options and recommendations [page]

  • Data preprocessing for ML with Google Cloud [page]

Google ML docs

  • Practitioners Guide to MLOps: A framework for continuous delivery and automation of machine learning [pdf] (May 2021)

  • Exploratory Data Analysis for Feature Selection in Machine Learning (2020) [pdf]

  • AI Explanations Whitepaper [pdf]

lazyprogrammer [site]

  • Coding Interview Questions: Row With Max Ones [post]

Abacus.AI [tweet], Santiago [tweet]

  • 5 courses in Coursera to become a Machine Learning engineer:

    1. Machine Learning

    2. Deep Learning Specialization

    3. TensorFlow Developer Professional Certificate

    4. TensorFlow: Advanced Techniques

    5. Introduction to Machine Learning In Production

Gus [tweet]

Do you want to learn Machine Learning?

There are many courses and tutorials online but these are the 3 I always recommend:

  • Intro to Machine Learning Crash Course

  • DeepLearning AI TensorFlow Developer Professional Certificate

  • TensorFlow Tutorials [link]