The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data. The information can deepen our understanding of how our world works—and help create better and “smarter” products. By IBM Data and AI Team.
Machine learning (ML) is a powerful tool that can be used to solve a wide range of problems. However, ML development and deployment can be a complex and challenging process. MLOps is a set of practices that combines ML with DevOps to streamline the ML development and deployment process.
The article explains:
- What is MLOps?
- Origins of the MLOps process
- How machine learning development works
- Steps in the MLOps process
- How generative AI is evolving MLOps
- MLOps use cases
- MLOps vs. DevOps
- MLOps vs. AIOps
One of the key benefits of MLOps is that it can help to reduce the time it takes to deploy ML models. This is because MLOps automates many of the tasks involved in the ML development and deployment process, such as model training, testing, and deployment. Good read!
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