Category: Courses
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Expert: Bayesian optimization & Hyperband
Bayesian Optimization and Hyperband are advanced techniques for hyperparameter tuning that balance exploration and computational efficiency. This post dives into their mathematical foundations, implementation details, and how modern frameworks like Ray Tune and Optuna combine them for large-scale machine learning optimization.
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Introduction to unit testing in Python
Learn the fundamentals of unit testing in Python, from the basics of the built-in unittest module to the powerful pytest framework. This guide walks through key concepts, examples, and best practices to help developers write reliable, maintainable, and production-ready tests.
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Best practices: domain ownership and federated governance
Discover how domain ownership and federated governance enable organizations to scale autonomy without losing control. This best-practice guide explores principles, architecture, and tooling strategies for implementing distributed accountability while maintaining global consistency.
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Tools: Feast, Hopsworks
Feature stores like Feast and Hopsworks have become the backbone of modern MLOps. This article explores how these tools streamline feature management, ensure consistency between training and inference, and empower teams to scale machine learning workflows efficiently.
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Expert: designing reusable components across domains
Designing reusable components across domains demands more than modular code—it requires deep architectural foresight, governance, and empathy for diverse contexts. This post explores proven design patterns, governance models, and strategies used by modern engineering organizations to achieve sustainable cross-domain reuse.
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Using FastAPI to build a mini REST API in Python
FastAPI has rapidly become one of the go-to frameworks for building high-performance REST APIs in Python. In this guide, we will walk through creating a mini REST API from scratch using FastAPI, explore how it compares with Flask and Django REST Framework, and learn practical tips for testing, documentation, and deployment.
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Introduction to streaming data architecture
Streaming data architecture is the backbone of modern real-time systems, powering everything from recommendation engines to IoT telemetry and financial analytics. This post introduces the core concepts, patterns, and tools behind streaming architectures, with practical insights on how to design scalable, fault-tolerant pipelines for real-world applications.
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Empirical: L1/L2 impact
Understanding the practical effects of L1 and L2 regularization goes far beyond the textbook explanation of sparsity versus smoothness. This post dives into empirical experiments, performance trade-offs, and the nuanced behaviors of these penalties across different model classes.
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Tools: Great Expectations, Soda Core, Deequ
Data quality validation is no longer an afterthought but a core component of modern data pipelines. This article explores three leading open-source frameworks — Great Expectations, Soda Core, and Deequ — that automate data validation, profiling, and continuous monitoring. We compare their architecture, integration capabilities, and practical strengths through empirical examples and real-world use cases…