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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Parameterizing Pipelines in Azure Data Factory
Parameters turn a pipeline that copies one table into a pipeline that copies any table. This guide explains the four places parameters live in Azure Data Factory, how a value travels from a trigger down to a connection, a worked generic copy in JSON, and where parameterising goes too far.
Airflow for ML Pipelines: A Practical Guide
Airflow can schedule more than data loads: it can retrain a model, score each day, check the error and raise an alert. This guide builds a training DAG and a scoring DAG on real bike rental data, and shows them handling a normal day, a bad day, and the day Hurricane Sandy cut the data off.
Vertex AI Pipelines for MLOps on GCP
A model trained once in a notebook goes stale. A pipeline retrains it the same way every time and decides whether the new one is good enough. This guide explains components, artifacts and the compiled graph on what was Vertex AI Pipelines, and compiles and runs a retraining pipeline with a promotion gate.