Category: Courses
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Expert: model capacity and overfitting trade-offs
Understanding model capacity and overfitting trade-offs is essential for designing high-performing, generalizable machine learning systems. This article explores how model capacity affects bias and variance, how overfitting manifests in deep models, and what strategies expert practitioners use to optimize complexity without sacrificing generalization.
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Best practices for ML API design
Designing APIs for machine learning systems requires combining software engineering rigor with data science insight. This article explores best practices for building scalable, maintainable, and reproducible ML APIs, covering versioning, schema management, performance, and lifecycle integration used by top tech companies.
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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.
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Tools: Snyk, Dependabot, and Checkov
By 2025, security automation using Snyk, Dependabot, and Checkov has become essential for DevSecOps workflows. This article explores how each tool contributes to vulnerability management, dependency maintenance, and infrastructure compliance within modern CI/CD pipelines.
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Empirical: OLTP vs OLAP query performance comparison
An empirical 2025 analysis comparing OLTP and OLAP systems across latency, throughput, and scalability metrics. The post benchmarks PostgreSQL, ClickHouse, and Snowflake, examining architectural trade-offs and real-world engineering implications.
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Best practices for imports and packaging in monorepos
By 2025, disciplined import and packaging strategies have become essential in managing large Python monorepos. This article provides a modern best-practice guide on structuring imports, defining clear boundaries, managing dependencies, and leveraging tools like Poetry, Pants, and Bazel for scalable development.
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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.
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Empirical: recursion vs iteration and memory impact
This post empirically examines recursion versus iteration in Python, analyzing their runtime behavior, memory consumption, and scalability. Using profiling and benchmarking tools, it identifies when recursion is practical and when iteration provides significant performance advantages.
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Introduction to infographics and visual learning for Python/ML
Visual learning is transforming how Python and machine learning practitioners communicate data and concepts. This post introduces infographics as a tool for better comprehension, exploring key libraries, design principles, and modern visualization workflows that enhance understanding and storytelling in data science.
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Tools: Prometheus, Grafana, Airflow sensors
Prometheus, Grafana, and Airflow sensors form the core of modern observability and orchestration in data engineering. This post explores how these tools interact, with practical examples, integration strategies, and best practices for building reliable, metrics-driven data pipelines.