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  How Azure’s ML Pipeline Helps in Automating Workflows Machine learning (ML) has become a critical component in today’s data-driven world, helping organizations extract valuable insights, improve decision-making, and boost productivity. However, building and deploying ML models is not a one-time task — it involves several stages, such as data preparation, model training, validation, deployment, and monitoring. This is where Azure’s ML pipeline becomes a game-changer. It simplifies and automates these complex workflows, making it easier to manage, scale, and reproduce ML projects efficiently.  How Azure’s ML Pipeline Helps in Automating Workflows What is an Azure ML Pipeline? An Azure ML pipeline is a series of configurable and repeatable steps used to build, train, and deploy machine learning models. It allows data scientists and ML engineers to define workflows that can be automated and reused. These pipelines are part of Azure Machine Learning , Microsoft’s cloud-ba...