Tag: Intro
-
Introduction to lakehouse architecture
The lakehouse architecture bridges the gap between data lakes and warehouses, providing unified governance, performance, and flexibility. This post introduces the core principles, benefits, and technologies behind lakehouses, explaining why theyâve become the foundation for modern data platforms in 2025.
-
Introduction to fairness in machine learning
Fairness in machine learning ensures that AI systems make equitable decisions across different groups. This beginner-friendly introduction covers key fairness definitions, sources of bias, core metrics, mitigation strategies, and the leading tools engineers use to build fair models.
-
Intro to scaling ML inference
Scaling machine learning inference efficiently is as critical as training a good model. As models grow larger and more complex, the challenge shifts from accuracy to throughput, latency, and cost optimization. This post introduces practical strategies, architectures, and tools used in 2025 to scale ML inference across CPUs, GPUs, and distributed environments.
-
Introduction to SOLID principles in Python
An introduction to SOLID principles for Python developers. Learn how to write cleaner, more modular, and maintainable code by applying the five foundational software design principlesâSRP, OCP, LSP, ISP, and DIPâwith practical Python examples and real-world best practices.
-
Intro to dimensionality reduction
Dimensionality reduction helps simplify complex datasets by reducing features while retaining essential information. This post introduces the fundamentals of PCA and other popular techniques like UMAP and t-SNE, explaining their mathematical foundations, real-world applications, and the latest tools driving high-performance data analysis in 2025.
-
Introduction to GRASP design principles
GRASP (General Responsibility Assignment Software Patterns) defines how to distribute responsibilities across classes and objects for maintainable, scalable software. This article introduces the nine GRASP principles with real-world examples and modern framework applications for engineers in 2025.
-
Introduction to stream processing concepts
Stream processing is at the core of real-time analytics and event-driven architectures. This article introduces stream processing concepts, explains key differences from batch processing, and highlights tools like Apache Kafka, Flink, and Materialize that enable continuous computation on live data streams.
-
Intro to natural language processing
Natural Language Processing (NLP) enables computers to understand and generate human language, powering modern applications like chatbots, search engines, and sentiment analysis. This beginner-friendly introduction explains key NLP concepts, preprocessing pipelines, modern libraries, and real-world use cases in 2025.
-
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.
-
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.