CHARLAS - Tracing Distribuido con OpenTelemetry
Explora el tracing distribuido con OpenTelemetry: conceptos, instrumentación automática y demostración práctica para monitorear sistemas distribuidos eficientemente.
Event-Driven Applications - Apache Kafka and Python
Explore event-driven applications using Apache Kafka and Python. Learn about Kafka's capabilities, integration techniques, and real-time data streaming for modern application development.
Reproducible and Maintainable Data Science Code with Kedro
Discover how Kedro, an open-source Python framework, transforms experimental data science code into production-ready ML pipelines, emphasizing reproducibility, maintainability, and modularity.
Creating Extensible Workflows with Off-Label Use of Python
Discover how to create flexible yet constrained workflow systems using Python's type annotations, coroutines, and dataclasses for extensible APIs in data processing, ETL, CI/CD, and more.
Patterns of ML Models in Production
Explore ML model deployment patterns for production, covering online serving, offline processing, and scalable frameworks like Ray Serve, with insights from 100+ user interviews.
Data Processing on Ray
Explore Ray's architectural changes for scaling Python applications in machine learning and data processing, addressing challenges in large-scale workloads.
From NumPy to PyTorch - A Story of API Compatibility
Explore PyTorch's approach to NumPy compatibility, its importance, challenges, and necessary divergences in this insightful discussion on API design and data science workflows.
From 3 to 300 FPS - NES Emulation in Python and Cython
Explore NES emulation in Python, from slow beginnings to 300+ fps using Cython. Learn optimization techniques and challenges in achieving real-time performance.
Effective Data Visualization
Mastering data visualization: from choosing plots to preprocessing datasets. Learn to effectively communicate data using Python libraries like seaborn, matplotlib, and plotly. Covers density estimation, dimensionality reduction, and interactive plotting.
Writing Performant Code for Modern Python Interpreters
Explore modern Python optimization techniques, learn new performance guidelines, and discover how to leverage language improvements for faster code execution.
Serialization - More Than Pickling
Explore alternatives to Python's pickle for object serialization, comparing efficiency and security considerations for different methods.
Writing Functional Code in Python
Explore functional programming concepts and their application in Python, enhancing code quality and efficiency through practical examples and key principles.
Dock Your Jupyter Notebook
Learn to containerize Jupyter Notebooks with Docker for reproducible and shareable machine learning projects, enabling smooth execution on remote machines.
Open Source on Easy Mode
Streamline open source project maintenance with automation tools and best practices for packaging, testing, CI/CD, documentation, and community engagement. Focus on the fun parts of development.
Enhancing Code Collaboration with Conflict Free Replicated Data Types
Explore conflict-free replicated data types (CRDTs) and their potential to enhance Jupyter notebooks, enabling real-time collaboration, synchronous editing, and improved version control in data science workflows.
Distributed Web Scraping in Python
Learn to build a distributed web scraper in Python, reducing batch job times and improving code durability. Explore techniques, lessons, and stories for efficient large-scale web scraping.
Software Development for Machine Learning in Python
Explore best practices for developing ML software in Python, focusing on abstraction, optimization, and production-ready code. Learn to design efficient, trackable, and easily maintainable ML systems.
Demystifying Python's Internals - Diving into CPython by Implementing a New Operator
Explore CPython's internals by implementing a pipe operator, covering tokenization, grammar, parsing, and bytecode generation for a deeper understanding of Python's core mechanics.
Employing NumPy's NPY Format for Faster Than Parquet DataFrame Storage
Explore faster DataFrame storage using NumPy's NPY format, outperforming Parquet in read/write times while supporting all DataFrame characteristics and NumPy dtypes.
Effective Protobuf - Everything You Wanted to Know, But Never Dared to Ask
Comprehensive exploration of Protocol Buffers, covering serialization, performance, encoding, and practical applications. Ideal for developers seeking to optimize data exchange in distributed systems.
Programming Your Way up a Skyscraper - Python in the Built World
Explore how architects use Python to enhance building design, from geometry editing to algorithmic design, data sorting, and software integration, unlocking new possibilities in architecture.