Google offers a comprehensive set of free courses that teach you the essentials of the Machine Learning Engineer's role  using Google Cloud's technologies.

Last year we took a look at Google's other AI-related Learning Path, that of Generative AI. Generative AI is a type of AI that can map long-range dependencies and patterns in large training sets, and then use what it learns to produce new content, including text, imagery, audio, and synthetic data.

That course, however, focused on Generative AI as the end product which is distinct from the actual role a ML engineer assumes. A ML engineer goes behind the scenes in designing, building, optimizing, operating, and maintaining ML systems, including the GenAI ones.

The "Machine Learning Engineer Learning Path" goes through all the responsibilities attached to the role, focusing on using Google Cloud's tools.

The path is comprised of 15 lengthy courses:

A Tour of Google Cloud Hands-on Labs
Get your feet wet by exploring the Google Cloud console and its basic features.

Introduction to AI and Machine Learning on Google Cloud
A rundown of the AI and ML tools available on Google Cloud that you can use.

Launching into Machine Learning
What Vertex AI AutoML is and how to build, train, and deploy an ML model without writing a single line of code.

TensorFlow on Google Cloud
Designing and building a TensorFlow input data pipeline as well as building ML models with TensorFlow and Keras.

Feature Engineering
Demonstrating how to improve the accuracy of ML models. It also includes labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

Machine Learning in the Enterprise
Real world applications of ML on business requirements and use cases.

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

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

Natural Language Processing on Google Cloud
Why you should learn NLP and an overview of the related material that follows.

Recommendation Systems on Google Cloud
NLP put to practice in Recommendation systems, detailing the different types and how to go about utilizing them.

Machine Learning Operations (MLOps): Getting Started
Deploy, evaluate, monitor and operate production ML systems.

Machine Learning Operations (MLOps) with Vertex AI: Manage Features
How to do MLOps, a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.

ML Pipelines on Google Cloud
Learn about a pipeline's components and its orchestration with TFX (TensorFlow Extended).

Prepare Data for ML APIs on Google Cloud
Various hand-on labs using Vertex and Python on how to prepare the data that is going to be used for training your ML model.

And finally, Build and Deploy Machine Learning Solutions on Vertex AI
Various hand-on labs using Vertex and Python on training, evaluating, tuning and deploying machine learning models.

The path is self paced as well free, and quite lengthy in duration since each individual course requires dedication for minimum 8 hours up to 32. Be aware that the material can be very technical at times.

Anyway, and to conclude, while the course might be focused on doing ML using Google's tools, it does have value beyond that since the concepts taught can be applied in general.

More Information

Machine Learning Engineer Learning Path

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