Why is MLOps important?
As the development and integration of ML models become more and more common in today’s world, the need for a robust operational framework has become more critical than ever. MLOps aims to address this requirement by streamlining the entire process of developing, deploying, and monitoring ML models. In this section, we will discuss the importance of MLOps due to the following aspects:
- Standardizing and automating ML workflows
MLOps aims to standardize and automate various stages of the ML life cycle, from data ingestion and preprocessing to model training, evaluation, and deployment. By doing so, it minimizes the likelihood of human errors, facilitates reproducibility, and improves overall efficiency. Google’s Vertex AI offers managed services for each stage of the ML workflow, which helps organizations achieve consistency, automate processes, and reduce operational overhead.
- Monitoring and managing model performance
One of the key aspects...
