The modern digital systems are becoming increasingly powered by Machine Learning models. The development of a model is, however, not all. Lifecycle management of machine learning models in production is a complicated problem.
It is here that MLOps (Machine Learning Operations) is necessary. MLOps is a combination of machine learning, DevOps, and Data Engineering practices to simplify the development, deployment, monitoring and maintenance of machine learning models.
What is MLOps?
MLOps (Machine Learning Operations) is a collection of principles that automate and oversees the whole lifecycle of machine learning systems. It guarantees that ML models are stable, scalable, and constantly developing after being implemented to production settings.
MLOps Architecture.
Real-World Applications of MLOps
Netflix, Amazon and Google companies are using the MLOps pipelines to operate large-scale machine learning systems including:
- Recommendation systems.
- Individualized product recommendations.
- Intelligent search systems.
Conclusion
MLOps is now a vital field that is needed to create trustworthy and scalable machine learning systems. As more organizations embrace AI technologies, hyper-celestial learning using machine learning models will be a crucial ability to be able to operationalize the models among engineers and data experts.
To learners and developers venturing into the AI sector, MLOps can be a good resource to understand how machine learning systems behave in the real world.
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