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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics β written by GloryTecks trainers in Hyderabad.
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Reproducibility in ML: Seeds, Configs and Pipelines
If a training run cannot be repeated, its results cannot be trusted or compared. This guide measures the main sources of variation on a real model, from missing seeds to row order, shows how much results move between seeds, and builds a small run manifest that records what a rerun needs.
Metadata-Driven Pipelines in Azure Data Factory
A metadata-driven pipeline reads a list of what to copy from a control table and applies one generic Copy activity to every row. This guide explains the building blocks in Azure Data Factory, shows the pattern working on real tables, and walks through the pipelines and control tables the Copy Data tool generates.
Power BI Deployment Pipelines for Teams
How Power BI teams use deployment pipelines: stages and capacity requirements, which items can be deployed, item pairing and the duplicates it can create, deployment rules that point each stage at the right data, why data needs refreshing after deployment, and a team workflow.
Event-Driven Pipelines with Lambda and S3
An event-driven pipeline starts work the moment a file arrives in S3. This guide explains S3 event notifications, writes a Lambda handler that validates and converts a real file, and runs it twice on the same event to show idempotency. It also covers encoded keys, loops, failure handling, and when to add a queue.
Azure Synapse Pipelines vs Standalone ADF
Synapse pipelines are built on Azure Data Factory, so most of what you know transfers. The gaps are few and specific: integration runtime sharing, the SSIS runtime, global parameters, Power Query and how deployment works. This guide lists each documented difference, what it means in practice, and how to choose.
Secrets Management for ML Pipelines
ML pipelines handle more credentials than most code, and leak them in places ordinary software does not, such as experiment trackers. This guide plants AWS's published example key in a project and follows it: a scanner flags it, MLflow stores it in plain text, a log filter redacts it and a vault keeps it out.