MLOps on GCP
11 November, 2024 – Amsterdam, The Netherlands
With this course, you will discover what MLOps is and how you can apply it in GCP (Google Cloud Platform). For example, you will learn more about Google Cloud’s Vertex AI, Cloud Run, and Cloud Function. This course is aimed at people with Python skills and general ML experience.
Looking to upskill your team(s) or organization?
Rozaliia will gladly help you further with custom training solutions.
Rozaliia Khafizova
Data and AI Training Advisor
Duration
2 days
Time
09:00 – 17:00
Language
English
Lunch
Included
Certification
No
Level
Professional
What will you learn?
After the training, you will be able to:
Understand all the necessary components in an end-to-end ML system
Set up Cloud Monitoring dashboards for your application
Create and trigger machine learning pipelines with Vertex AI and Cloud Functions
Integrate and deploy all code through a CI/CD pipeline with Github Actions
Deploy your model as scalable API with FastAPI, Docker and Cloud Run
Program
- Discover key MLOps principles
- Creating a solution design
- Data versioning, environments and compute targets
- ML training jobs
- Experiment tracking
This training is for you if:
You already have a solid understanding of ML, and want to take your models outside of the development phase.
You want to incorporate best practices from Software Engineering
You already have foundational software engineering skills (Python, Git, Docker)
You want to learn more about GCP and MLOps.
This training is not for you if:
You want to learn how to develop ML models (check out the Certified Data Science with Python or the Advanced Data Science with Python trainings)
You do not have basic programming experience (check out our Python for Data Analyst course)
You want to learn general methods for developing production-ready applications without focusing on a specific public cloud (check out our Production-Ready Machine Learning course)
Your primary interest is in (exploratory) research; this course is geared towards ML engineering
You are interested in a different cloud service (check out our MLOps on Azure training or our MLOps on AWS training)
Why should I do this training?
Learn best practices about deploying machine-learning applications on GCP
Hands-on training with real life examples – learn today apply tomorrow!
Get taught by machine-learning experts that love to teach in a very fun and interactive learning environment
What else should I know?
After registering for this training, you will receive a confirmation email with practical information. A week before the training, the trainer will get in touch to ask you about any requirements you may have and any pre-course tasks you will need to do.
See you soon!
Training information
All literature and course materials are included in the price
Information on the software and tooling will be shared before the start date
This course requires a laptop
Online courses are delivered via Zoom or Microsoft Teams
Meet the Trainers
Yke Rusticus
Yke is an experienced machine learning engineer who has helped various companies generate value from their machine learning solutions. As trainer, his goal is to make sure you learn how to get your solutions beyond the experimentation phase, and into the real world.
Ismael Cabral Violante
Ismael Cabral is a Machine Learning Engineer and Airflow trainer at Xebia Data in The Netherlands. At the same time, he is busy co-authoring the new version of “Data Pipelines with Apache Airflow”.
Also interesting for you
View all training coursesFollow our Production-Ready Python for Machine Learning training course to bring your machine learning models into production.
Learn in this Machine Learning System Design training how to design, reason, and communicate about larger ML systems.
This MLOps on Azure training is then a perfect next step if you’d like like to take your Machine Learning models further.
