Training schedule
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Contact Giovanni Lanzani, if you want to know more about custom data & AI training for your teams. He’ll be happy to help you!
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Apply MLOps principles in the public cloud
During this training, you will learn how to apply MLOps principles such as continuous training,
continuous deployment and end-to-end monitoring to build end-to-end solutions in one of the
public clouds (AWS, Azure or GCP).
This training is for you if…
- Already have a solid understanding of ML, and want to take your models outside of the development phase.
- Already have basic SWE skills (Basic understanding of Docker, Python, Git).
- Want to incorporate best practices from Software Engineering.
- Want to learn more about the Cloud.
This training is not for you if…
- Want to learn more about developing ML models itself (this knowledge is already assumed).
- Do not have basic programming experience. In that case, an introductory course is advised.
- Are mainly interested in doing (exploratory) research. This course is much oriented towards ML engineering.
Clients we've helped
What you'll learn
MLOps
- Have a solid understanding of all the necessary components in an ML system. Including best practices, common design challenges, etc.
- Create a Machine Learning Pipeline In AzureML.
- Deploy your model on Azure as scalable API on Azure Container Instances
- Integrate and deploy all code through a CI/CD pipeline in Azure DevOps
The schedule
- Key MLOps principles
- Creating a solution design
- Building an ML pipeline
- Deploying an ML pipeline with CI/CD
- Scheduling an ML pipeline for automated training
- Tracking trained models and their metrics
- Deploying models as REST APIs
learning journey
Machine Learning Engineering Learning Journey
Julian de Ruiter
Machine learning engineer
Structured, to-the-point, good combination of theory and practical examples, very knowledgeable trainer who can explain concepts very well
It was a hands-on and tangible course. We could apply what we learned in a matter of minutes. The trainer did a great job of answering ad-hoc questions that complemented the material. We appreciated the fact that we could apply what we were taught directly to our company.
I liked every aspect of this training and would like to thank the trainers. They did an excellent job of explaining how to use Spark for data science. This is the fourth GoDataDriven training I’ve followed. All were great, but this was the best one so far.
Climbing a steep Python and Machine Learning curve in three days. This would have taken me months on my own.