Using Azure ML for MLOps
There are many different platforms for orchestrating your MLOps. Here, we will just focus on one tool, Azure ML. As a comprehensive cloud-based platform, Azure ML can play a significant role in various stages of the MLOps pipeline, fitting seamlessly into your existing framework of data ingestion, storage, development, deployment, validation, and monitoring. Here’s how Azure ML integrates with each of these stages:
- Data ingestion: Azure ML supports various data sources, allowing for flexible data ingestion. It can connect to Azure Data Lake, Azure Blob Storage, and other external sources. This flexibility ensures that data ingestion, a critical first step in the MLOps pipeline, is streamlined and efficient.
- Data storage: With Azure ML, data storage is integrated with Azure’s cloud storage solutions. It allows for the secure and scalable storage of large datasets, essential for ML workflows. This integration facilitates easy access...
