Google Cloud Professional Machine Learning Engineer Training Course: Industrialise your models on Vertex AI

The Google Cloud Professional Machine Learning Engineer training course is for people who know how to train a model and are discovering that this was not the hard part. A model running in a notebook is a prototype; what the certification assesses, and what the job demands, is a model in production — versioned, monitored, retrained, and whose decisions you can explain.

Over 3 days, you walk the whole chain on Vertex AI: framing a business problem as an ML problem (the step people skip, and the one that costs most), preparing data and managing the Feature Store, training at scale, orchestrating with Vertex AI Pipelines, deploying for online and batch prediction, then monitoring drift — in the data and in the predictions. Responsibility and explainability are not decoration: they are on the exam.

This course extends our Google Cloud Professional Data Engineer Training Course, which builds the pipelines that feed the models, and our Google Cloud Generative AI Leader Training Course on the generative side. The Google Cloud foundations are laid by our Google Cloud Associate Cloud Engineer Training Course and our Google Cloud Professional Cloud Architect Training Course. On the data side, our Google BigQuery Training Course covers BigQuery ML and training in SQL. If you need to shore up the basics, our Python Training Course is the natural prerequisite. To compare ecosystems, see our Azure Databricks Training Course and our NVIDIA Deep Learning Training Course. Finally, containerised model deployment connects to our Google Kubernetes Engine Training Course.

Sessions are guaranteed from a single registrant (except in cases of force majeure). A preliminary discussion takes place between the participant and/or a company representative to fully take into account the participant’s profile. Assessment: quizzes, role-play and practical exercises. A certificate of completion is issued at the end of the training. This training prepares you for the Google Cloud Professional Machine Learning Engineer certification (exam not included). This training is part of our Cloud Computing Training Courses catalogue. Discover our other cloud training courses to master architectures, services and best practices on AWS, Azure and GCP. To get started on AWS, we recommend our AWS Cloud Practitioner Training Course as a prerequisite, which lays the foundations of the Amazon Cloud.

By the end of the training, participants will be able to:

• Translate a business problem into a machine learning problem — and recognise when it is not one.
• Prepare and manage training data, and use a Feature Store.
• Train models on Vertex AI (AutoML, custom training, hyperparameter tuning).
• Orchestrate a reproducible ML pipeline with Vertex AI Pipelines.
• Deploy a model for online and batch prediction, and size the inference.
• Monitor data and prediction drift, and trigger retraining.
• Apply explainability, fairness and model governance requirements.

• Two prerequisites are genuinely blocking. First Python: you must be able to write and read code and manipulate data with pandas, without discovering the language during the course. Second, the fundamentals of machine learning: supervised learning, train and test sets, overfitting, evaluation metrics. We do not teach ML theory; we teach how to put it into production.
• Some initial Google Cloud experience (projects, IAM, Cloud Storage) is expected. The Associate Cloud Engineer level is amply sufficient.
• Because every participant is unique, a personalised discussion with our expert allows us to design a training course perfectly aligned with their objectives.

• Data scientists who need to move their models from notebook to production.
• ML and MLOps engineers working on Google Cloud.
• Data engineers extending their scope into machine learning.
• Architects and technical leads preparing for the Professional ML Engineer certification.