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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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Production ML Systems: Architecture and Best Practices
In a production machine learning system the model is the smallest part. This guide maps the components around it, compares the three serving patterns, explains the ML-specific technical debt that architecture has to contain, and sets out the practices that keep a system maintainable as it grows.
Distributed Systems Basics for Data Engineers
The distributed systems ideas behind Spark, Kafka and cloud warehouses, for data engineers: partitioning and hot keys, why modulo hashing moved 74% of real taxi data when a node was added and consistent hashing 23%, replication, what CAP really says, failures and retries, and why clocks cannot be trusted.
Logging and Observability for ML Systems
Metrics show that something changed; logs and traces show which requests, which step and which model version. This guide logs every prediction of a real intent classifier as JSON, traces each request with OpenTelemetry and joins the true labels later, finding that 9% of requests held 41% of the errors.
Recommendation Systems: How Netflix and Amazon Suggest Items
How recommendation systems work, from the ideas behind Amazon's item-to-item filtering and the Netflix Prize to a working recommender built on a real online shop's transactions, then tested honestly against simple baselines, which turn out to be much harder to beat than most tutorials suggest.