Google's Machine Learning Engineer Learning Path -

Google offers a comprehensive free course to learn the fundamentals of a Machine Learning Engineer role using Google Cloud technologies.

Last year, we looked at another AI-related learning path from Google: Generative AI, a type of AI that can map long-distance dependencies and patterns in large training sets and use what it learns to create new content, including text, images, audio, and synthetic data.

However, that course focuses on generative AI as an end product, which is different from the actual role of an ML engineer, who works behind the scenes to design, build, optimize, operate, and maintain ML systems, including GenAI systems.

The Machine Learning Engineer learning path will teach you all the responsibilities that come with the role, with an emphasis on using Google Cloud tools.

The course is made up of 15 long courses.

Tour the Google Cloud Hands-on Labs
Explore the Google Cloud console and its basic features to get started.

Introduction to AI and Machine Learning on Google Cloud
An overview of AI and ML tools available on Google Cloud.

Enter machine learning
Learn what Vertex AI AutoML is and how to build, train, and deploy ML models without writing a single line of code.

TensorFlow on Google Cloud
Design and build a TensorFlow input data pipeline and build an ML model using TensorFlow and Keras.

Feature Engineering
Learn how to improve the accuracy of your ML models, including labs on feature engineering with BigQuery ML, Keras, and TensorFlow.

Machine Learning in the Enterprise
Real-world application of ML in business requirements and use cases.

Production Machine Learning System
How to design and build your own ML systems.

Computer Vision Fundamentals with Google Cloud,
Use Vertex AI and AutoML.

Natural Language Processing on Google Cloud
Why you should study NLP, followed by an overview of related material.

Recommendation System on Google Cloud
We will put NLP into practice for recommendation systems, going into detail about the different types and how to utilize them.

Machine Learning Operations (MLOps): An Introduction
Deploy, evaluate, monitor, and operate ML systems.

Machine Learning Operations (MLOps) with Vertex AI: Feature Management
How to do MLOps, a discipline focused on deploying, testing, monitoring, and automating ML systems in production environments.

Google Cloud ML Pipeline
Learn about pipeline components and orchestration using TFX (TensorFlow Extended).

Preparing data for Google Cloud's ML APIs
Various hands-on labs using Vertex and Python on how to prepare data for training ML models.

And finally, building and deploying machine learning solutions. About Vertex AI
A range of hands-on labs to train, evaluate, tune, and deploy machine learning models using Vertex and Python.

The courses are free and self-paced, but are fairly long in duration, with each course requiring a minimum of 8 hours and a maximum of 32 hours of intensive study. Be aware that the material can get very technical.

Anyway, the bottom line is that although this course is focused on doing ML using Google's tools, it's worth much more than that as the concepts taught are generally applicable.

For more information

Machine Learning Engineer Learning Path

Follow Google's Generative AI learning path

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