MLOps, Machine Learning Operations for beginners
Learn the lifecycle of a Machine Learning model and how to put it into production with MLOps. Perfect for Data Scientists, ML Engineers, and Software Engineers.
What you’ll learn
- Understand the lifecycle of a Machine Learning model
- Gain the best practices for putting Machine Learning models in production
- Leverage the power of MLOps to productionalise Machine Learning models at scale
- Get some insights on how to choose your perfect MLOps stack
This course is about Machine Learning Operations.
Machine Learning and Artificial Intelligence have became a hot topic in recent years. Numerous techniques and algorithms were developed and proved their efficiencies in addressing business issues and bringing value to companies. Take fraud detection, recommendation systems or autonomous vehicles, etc. as examples.
However, most of the developed machine learning models do not go to production! Among others, this is due to one major reason: Machine Learning models are not classical software. The existing frameworks and methodologies that work for classical software proved to be inadequate with Machine Learning models. Hence, new paradigms and concepts should be brought to handle the specificities of Machine Learning Algorithms.
This course is addressed to Data professionals (Data Scientists, Data Engineers, Machine Learning Engineers and Software Engineers) as well as to everyone who want to understand the lifecycle of a Machine Learning model from experimentation to production. In this course, wa re going to see the best practices and recommended ways to put machine learning models into production. This will allow us also to see how we can leverage the power of MLOps to deploy Machine Learning at scale. Finally, as deploying models is about tooling, we are going to have a look on how to choose its perfect stack when adopting Machine Learning Operations best practices.
Wish you a nice journey!
Who this course is for:
- Everyone
- Data Scientists
- Machine Learning Engineers
- Software Engineers
- DevOps Engineers
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